Photon-assisted electron transmission resonance through a quantum well with spin-orbit coupling

Using the effective-mass approximation and Floquet theory, we study the electron transmission over a quantum well in semiconductor heterostructures with Dresselhaus spin-orbit coupling and an applied oscillation field. It is demonstrated by the numerical evaluations that Dresselhaus spin-orbit coupling eliminates the spin degeneracy and leads to the splitting of asymmetric Fano-type resonance peaks in the conductivity. In turn, the splitting of Fano-type resonance induces the spin- polarization-dependent electron-current. The location and line shape of Fano-type resonance can be controlled by adjusting the oscillation frequency and the amplitude of external field as well. These interesting features may be a very useful basis for devising tunable spin filters.Comment: 10pages,4figure

Tunnel splitting and quantum phase interference in biaxial ferrimagnetic particles at excited states

The tunneling splitting in biaxial ferrimagnetic particles at excited states with an explicit calculation of the prefactor of exponent is obtained in terms of periodic instantons which are responsible for tunneling at excited states and is shown as a function of magnetic field applied along an arbitrary direction in the plane of hard and medium axes. Using complex time path-integral we demonstrate the oscillation of tunnel splitting with respect to the magnitude and the direction of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings . The oscillation is gradually smeared and in the end the tunnel splitting monotonously increases with the magnitude of the magnetic field when the direction of the magnetic field tends to the medium axis. The oscillation behavior is similar to the recent experimental observation with Fe$_8$ molecular clusters. A candidate of possible experiments to observe the effect of quantum phase interference in the ferrimagnetic particles is proposed.Comment: 15 pages, 5 figures, acceptted to be pubblished in Physical Review

A brain-region-based meta-analysis method utilizing the Apriori algorithm

Background: Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. Results: In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. Conclusions: The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis

Molecular cavity topological representation for pattern analysis: A NLP analogy-based word2vec method

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension reduction and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities

Expression analysis of four flower-specific promoters of Brassica spp. in the heterogeneous host tobacco

The 5’-flanking region of ca. 1200 bp upstream of the translation start site (TSS) of a putative cell wall protein gene was cloned from Brassica campestris, B. chinensis, B. napus and B. oleracea, and transferred to tobacco via Agrobacterium-mediation after fused to promoter-less beta-glucuronidase(GUS) reporter gene. Histochemical GUS staining and fluorometric quantification of the transgenic tobacco showed that all four promoters conferred GUS expression in petal, anther, pollen and stigma ofthe flower, not in any vegetative organs or tissues of the plants. A series of 5’-end deletion of the promoter from B. napus disclosed that the region -104 to -17 relative to TSS was sufficient to confer flower-specific expression, and the region -181 to -161 played a key role in maintaining strong drivingpower of the promoter. Besides, several enhancer and suppressor regions were also identified in the promoter

Flexible unsupervised feature extraction for image classification

Dimensionality reduction is one of the fundamental and important topics in the fields of pattern recognition and machine learning. However, most existing dimensionality reduction methods aim to seek a projection matrix W such that the projection W T x is exactly equal to the true low-dimensional representation. In practice, this constraint is too rigid to well capture the geometric structure of data. To tackle this problem, we relax this constraint but use an elastic one on the projection with the aim to reveal the geometric structure of data. Based on this context, we propose an unsupervised dimensionality reduction model named flexible unsupervised feature extraction (FUFE) for image classification. Moreover, we theoretically prove that PCA and LPP, which are two of the most representative unsupervised dimensionality reduction models, are special cases of FUFE, and propose a non-iterative algorithm to solve it. Experiments on five real-world image databases show the effectiveness of the proposed model

Macroscopic Quantum Phase Interference in Antiferromagnetic Particles

The tunnel splitting in biaxial antiferromagnetic particles is studied with a magnetic field applied along the hard anisotropy axis. We observe the oscillation of tunnel splitting as a function of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings. The oscillation is similar to the recent experimental result with Fe}$_8$\textrm{\ molecular clusters.}Comment: 8 pages, 2 postscript figures, to appear in J. Phys.: Condes. Matte