Anomalous Hall effect in L10-MnAl films with controllable orbital two-channel Kondo effect

The anomalous Hall effect (AHE) in strongly disordered magnetic systems has been buried in persistent confusion despite its long history. We report the AHE in perpendicularly magnetized L10-MnAl epitaxial films with variable orbital two-channel Kondo (2CK) effect arising from the strong coupling of conduction electrons and the structural disorders of two-level systems. The AHE is observed to excellently scale with pAH/f=a0pxx0+bpxx2 at high temperatures where phonon scattering prevails. In contrast, significant deviation occurs at low temperatures where the orbital 2CK effect becomes important, suggesting a negative AHE contribution. The deviation of the scaling agrees with the orbital 2CK effect in the breakdown temperatures and deviation magnitudes

Quantum state engineering with flux-biased Josephson phase qubits by Stark-chirped rapid adiabatic passages

In this paper, the scheme of quantum computing based on Stark chirped rapid adiabatic passage (SCRAP) technique [L. F. Wei et al., Phys. Rev. Lett. 100, 113601 (2008)] is extensively applied to implement the quantum-state manipulations in the flux-biased Josephson phase qubits. The broken-parity symmetries of bound states in flux-biased Josephson junctions are utilized to conveniently generate the desirable Stark-shifts. Then, assisted by various transition pulses universal quantum logic gates as well as arbitrary quantum-state preparations could be implemented. Compared with the usual PI-pulses operations widely used in the experiments, the adiabatic population passage proposed here is insensitive the details of the applied pulses and thus the desirable population transfers could be satisfyingly implemented. The experimental feasibility of the proposal is also discussed.Comment: 9 pages, 4 figure

The Y-box factor ZONAB/DbpA associates with GEF-H1/Lfc and mediates Rho-stimulated transcription

Epithelial tight junctions recruit different types of signalling proteins that regulate cell proliferation and differentiation. Little is known about how such proteins interact functionally and biochemically with each other. Here, we focus on the Y-box transcription factor ZONAB (zonula occludens 1-associated nucleic-acid-binding protein)/DbpA (DNA-binding protein A) and the Rho GTPase activator guanine nucleotide exchange factor (GEF)-H1/Lbc's first cousin, which are two tight-junction-associated signalling proteins that regulate proliferation. Our data show that the two proteins interact and that ZONAB activity is Rho-dependent. Overexpression of GEF-H1 induces accumulation of ZONAB in the nucleus and activates transcription. Microtubule-affinity regulating kinase/partition-defective-1, another type of GEF-H1-associated signalling protein, remains in the cytoplasm and partially co-localizes with the exchange factor. GEF-H1 and ZONAB are required for expression of endogenous cyclin D1, a crucial RhoA signalling target gene, and GEF-H1-stimulated cyclin D1 promoter activity requires ZONAB. Our data thus indicate that GEF-H1 and ZONAB form a signalling module that mediates Rho-regulated cyclin D1 promoter activation and expression

Velocity estimation error reduction in stenosis areas using a correlation correction method

The advent of ultrafast ultrasound imaging proved beneficial for capturing transient flow patterns which was never readily achievable before. Velocity estimation methods based on 2D block-matching outperform Doppler based methods by offering higher frame rate with the cost of increased uncertainty in presence of out-of-plane motion as a result of turbulent flow. Local median filtering can partially address the estimation error reduction in stenosis areas at the risk of higher inaccuracy, since neighboring values may be also outliers. In this study, a correlation correction method is proposed, where the out-of-plane motion is eliminated by means of multiplying correlation maps from a same area but in two adjacent pairs of RF images. Experimental investigations were performed on a wall-less flow phantom, and proposed method achieved an error reduction of 66% in turbulent flow regions

Dynamics of Vibrated Granular Monolayers

We study statistical properties of vibrated granular monolayers using molecular dynamics simulations. We show that at high excitation strengths, the system is in a gas state, particle motion is isotropic, and the velocity distributions are Gaussian. As the vibration strength is lowered the system's dimensionality is reduced from three to two. Below a critical excitation strength, a gas-cluster phase occurs, and the velocity distribution becomes bimodal. In this phase, the system consists of clusters of immobile particles arranged in close-packed hexagonal arrays, and gas particles whose energy equals the first excited state of an isolated particle on a vibrated plate.Comment: 4 pages, 6 figs, revte

Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods