In this paper, we consider the convergence of an abstract inexact nonconvex and nonsmooth algorithm, which cannot be included in previous framework. We promise a pseudo sufficient descent condition and a pseudo relati...
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In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently appearing in signal processing and machine learning research. The traditional alternating direction method of multipliers encou...
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Based on density functional theory, we computational designed the hetero junction composed by GeSe monolayer and graphene. The effects of interlayer coupling, strains and electric fields on the electronic structures o...
Based on density functional theory, we computational designed the hetero junction composed by GeSe monolayer and graphene. The effects of interlayer coupling, strains and electric fields on the electronic structures of the designed GeSe/graphene (G/g) hetero structure are explored. We demonstrated that both the intrinsic electric properties of the GeSe monolayer and graphene are well preserved in G/g hetero structure. It is found that an energy gap of 0.1. eV in graphene is opened by decreasing the interlayer distance in G/g hetero structure. The height of Schottky barrier can be effectively tuned by the interlayer distance between GeSe monolayer and graphene. Moreover, we found that applying in-plane strains and the electric fields perpendicular to the G/g hetero structure can control the Schottky barriers at the G/g interface. Our results predict that the ultra-thin G/g hetero structure can be used as two-dimensional semiconductor-based optoelectronic devices.
First-principles calculations are performed to study the dual effect of spin orbital coupling and electric fields on the structure and electronic properties of stanane (hydrogen saturated stanene). For the relaxed con...
First-principles calculations are performed to study the dual effect of spin orbital coupling and electric fields on the structure and electronic properties of stanane (hydrogen saturated stanene). For the relaxed configurations, it is found that spin orbital coupling effect has little impact on the geometry of stanane. However, the total energies of the stanane under electric fields are decreased. We also found that the transferred charge is mainly accumulated around the hydrogen atoms, indicating tin atoms are the charge donor in stanane. Linear response calculations prove the phase stability of stanane. Our results also indicate that the fundamental energy gap in stanane can be tuned by electric fields.
BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have b...
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BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information.
RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591.features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets.
CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden
In this article a theoretical framework for the Galerkin finite element approximation to the time-dependent Riesz tempered fractional problem is provided without the fractional regularity assumption. Because the time-...
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A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal tempora...
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Protein folding is one of the most important problems in molecular biology. The kinetic order of protein folding is one of the main aspects of the folding process. Previous methods for predicting protein folding kinet...
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In recent years, researchers have proposed several methods to transform time series (such as those of fractional Brownian motion) into complex networks. In this paper, we construct horizontal visibility networks (HVNs...
In recent years, researchers have proposed several methods to transform time series (such as those of fractional Brownian motion) into complex networks. In this paper, we construct horizontal visibility networks (HVNs) based on the -stable Lévy motion. We aim to study the relations of multifractal and Laplacian spectrum of transformed networks on the parameters and of the -stable Lévy motion. First, we employ the sandbox algorithm to compute the mass exponents and multifractal spectrum to investigate the multifractality of these HVNs. Then we perform least squares fits to find possible relations of the average fractal dimension , the average information dimension and the average correlation dimension against using several methods of model selection. We also investigate possible dependence relations of eigenvalues and energy on , calculated from the Laplacian and normalized Laplacian operators of the constructed HVNs. All of these constructions and estimates will help us to evaluate the validity and usefulness of the mappings between time series and networks, especially between time series of -stable Lévy motions and HVNs.
The atomic structure of recently synthesized thiolate-protected Au cluster is theoretically predicted via a simple structural rule summarized from the crystal structures of thiolate-protected Au（SR）, Au（SR） and Au...
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The atomic structure of recently synthesized thiolate-protected Au cluster is theoretically predicted via a simple structural rule summarized from the crystal structures of thiolate-protected Au（SR）, Au（SR） and Au（SR） clusters. We find that the Au（SR）（N = 7） and recently reported Au（SR）（N = 4）, Au（SR）（N = 3）, Au（SR）（N = 2） and Au（SR）（N = 1. belong to a family of homologous Au（SR） cluster whose Au-cores follow one-dimensional polytetrahedral growth pathway. The Au（SR） cluster is predicted to contain an anisotropic face-centered cubic（FCC） Au-core, which can be viewed as combination of two helical tetrahedra-Au4 chains and is remarkably different from the well-known spherical Au-core in ligand protected gold clusters in the size region of 1.2 nm. The intense near infrared（NIR） absorption of Au（SR） is attributed to the synergistic effect of anisotropic Au-core structure and ligand protections. A plausible cluster-to-cluster transformation mechanism is further suggested.
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