Surface morphology and magnetic anisotropy in (Ga,Mn)As

Atomic Force Microscopy and Grazing incidence X-ray diffraction measurements have revealed the presence of ripples aligned along the $[1\bar{1}0]$ direction on the surface of (Ga,Mn)As layers grown on GaAs(001) substrates and buffer layers, with periodicity of about 50 nm in all samples that have been studied. These samples show the strong symmetry breaking uniaxial magnetic anisotropy normally observed in such materials. We observe a clear correlation between the amplitude of the surface ripples and the strength of the uniaxial magnetic anisotropy component suggesting that these ripples might be the source of such anisotropy.Comment: 3 pages, 4 figures, 1 table. Replaced with published versio

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans

Simultaneous multiwavelength observations of the Low/Hard State of the X-ray transient source SWIFT J1753.5-0127

We report the results of simultaneous multiwavelength observations of the X-ray transient source SWIFT J1753.5-0127 performed with INTEGRAL, RXTE, NTT, REM and VLA on 2005 August 10-12. The source, which underwent an X-ray outburst since 2005 May 30, was observed during the INTEGRAL Target of Opportunity program dedicated to new X-ray novae located in the Galactic Halo. Broad-band spectra and fast timing variability properties of SWIFT J1753.5-0127 are analyzed together with the optical, near infra-red and radio data. We show that the source was significantly detected up to 600 keV with Comptonization parameters and timing properties typical of the so-called Low/Hard State of black hole candidates. We build a spectral energy distribution and we show that SWIFT J1753.5-0127 does not follow the usual radio/X-ray correlation of X-ray binaries in the Low/Hard State. We give estimates of distance and mass. We conclude that SWIFT J1753.5-0127 belongs to the X-ray nova class and that it is likely a black hole candidate transient source of the Galactic Halo which remained in the Low/Hard State during its main outburst. We discuss our results within the context of Comptonization and jet models.Comment: Accepted for publication in ApJ, 25 pages, 4 tables, 11 figures (3 in color

Thermal ionization of excitons in V-shaped quantum wires

The exciton-to-free-carrier transition in GaAs and In_xGa_{1-x}As V-shaped quantum wires is revealed by means of temperature-dependent magnetoluminescence experiments. The experimental results are in excellent agreement with the diamagnetic shift obtained from a solution of the full two-dimensional Schrödinger equation for electrons and holes including magnetic-field and excitonic effects. In the GaAs wires, the exciton-to-free-carrier transition is found to occur at temperature consistent with the exciton binding energies. In the In_xGa_{1-x}As wires the diamagnetic shift of the luminescence is found to be free-carrier-like, independent of temperature, due to the weakening of the exciton binding energy induced by the internal piezoelectric field

Tree Tensor Network State with Variable Tensor Order: An Efficient Multireference Method for Strongly Correlated Systems

[Image: see text] We study the tree-tensor-network-state (TTNS) method with variable tensor orders for quantum chemistry. TTNS is a variational method to efficiently approximate complete active space (CAS) configuration interaction (CI) wave functions in a tensor product form. TTNS can be considered as a higher order generalization of the matrix product state (MPS) method. The MPS wave function is formulated as products of matrices in a multiparticle basis spanning a truncated Hilbert space of the original CAS-CI problem. These matrices belong to active orbitals organized in a one-dimensional array, while tensors in TTNS are defined upon a tree-like arrangement of the same orbitals. The tree-structure is advantageous since the distance between two arbitrary orbitals in the tree scales only logarithmically with the number of orbitals N, whereas the scaling is linear in the MPS array. It is found to be beneficial from the computational costs point of view to keep strongly correlated orbitals in close vicinity in both arrangements; therefore, the TTNS ansatz is better suited for multireference problems with numerous highly correlated orbitals. To exploit the advantages of TTNS a novel algorithm is designed to optimize the tree tensor network topology based on quantum information theory and entanglement. The superior performance of the TTNS method is illustrated on the ionic-neutral avoided crossing of LiF. It is also shown that the avoided crossing of LiF can be localized using only ground state properties, namely one-orbital entanglement

The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)

Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction