Ab-initio calculations of spin tunneling through an indirect barrier

We use a fully relativistic layer Green's functions approach to investigate spin-dependent tunneling through a symmetric indirect band gap barrier like GaAs/AlAs/GaAs heterostructure along [100] direction. The method is based on Linear Muffin Tin Orbitals and it is within the Density Functional Theory (DFT) in the Local Density Approximation (LDA). We find that the results of our {\it ab-initio} calculations are in good agreement with the predictions of our previous empirical tight binding model [Phys. Rev. {\bf B}, 075313 (2006)]. In addition we show the $k_{||}$-dependence of the spin polarization which we did not previously include in the model. The {\it ab-initio} calculations indicate a strong $k_{||}$-dependence of the transmission and the spin polarization due to band non-parabolicity. A large window of 25-50 % spin polarization was found for a barrier of 8 AlAs monolayers at $k_{||}$ = 0.03 $2\pi/a$. Our calculations show clearly that the appearance of energy windows with significant spin polarization depends mostly on the location of transmission resonances and their corresponding zeros and not on the magnitude of the spin splitting in the barrier.Comment: 10 pages, 3 figure

Collective flow of open and hidden charm in Au+Au collisions at s\sqrt{s} = 200 GeV

We study the collective flow of open charm mesons and charmonia in Au+Au collisions at $\sqrt{s}$ = 200 GeV within the hadron-string-dynamics (HSD) transport approach. The detailed studies show that the coupling of $D,\bar{D}$ mesons to the light hadrons leads to comparable directed and elliptic flow as for the light mesons. This also holds approximately for $J/\Psi$ mesons since more than 50% of the final charmonia for central and mid-central collisions stem from $D+\bar{D}$ induced reactions in the transport calculations. The transverse momentum spectra of $D,\bar{D}$ mesons and $J/\Psi$'s are only very moderately changed by the (pre-)hadronic interactions in HSD which can be traced back to the collective flow generated by elastic interactions with the light hadrons.Comment: 9 pages, 8 figures, Phys. Rev. C, in pres

An Enhanced Features Extractor for a Portfolio of Constraint Solvers

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques

Maximum Resilience of Artificial Neural Networks

The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximal amount of input or sensor perturbation which is still tolerated. This problem of computing maximal perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximal resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some typos, rephrase the definition, and add some more existing wor

Integer quantum Hall effect and topological phase transitions in silicene

We numerically investigate the effects of disorder on the quantum Hall effect (QHE) and the quantum phase transitions in silicene based on a lattice model. It is shown that for a clean sample, silicene exhibits an unconventional QHE near the band center, with plateaus developing at $\nu=0,\pm2,\pm6,\ldots,$ and a conventional QHE near the band edges. In the presence of disorder, the Hall plateaus can be destroyed through the float-up of extended levels toward the band center, in which higher plateaus disappear first. However, the center $\nu=0$ Hall plateau is more sensitive to disorder and disappears at a relatively weak disorder strength. Moreover, the combination of an electric field and the intrinsic spin-orbit interaction (SOI) can lead to quantum phase transitions from a topological insulator to a band insulator at the charge neutrality point (CNP), accompanied by additional quantum Hall conductivity plateaus.Comment: 7 pages, 4 figure