14,212 research outputs found
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
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