Global analysis of quadrupole shape invariants based on covariant energy density functionals

Coexistence of different geometric shapes at low energies presents a universal structure phenomenon that occurs over the entire chart of nuclides. Studies of the shape coexistence are important for understanding the microscopic origin of collectivity and modifications of shell structure in exotic nuclei far from stability. The aim of this work is to provide a systematic analysis of characteristic signatures of coexisting nuclear shapes in different mass regions, using a global self-consistent theoretical method based on universal energy density functionals and the quadrupole collective model. The low-energy excitation spectrum and quadrupole shape invariants of the two lowest $0^{+}$ states of even-even nuclei are obtained as solutions of a five-dimensional collective Hamiltonian (5DCH) model, with parameters determined by constrained self-consistent mean-field calculations based on the relativistic energy density functional PC-PK1, and a finite-range pairing interaction. The theoretical excitation energies of the states: $2^+_1$, $4^+_1$, $0^+_2$, $2^+_2$, $2^+_3$, as well as the $B(E2; 0^+_1\to 2^+_1)$ values, are in very good agreement with the corresponding experimental values for 621 even-even nuclei. Quadrupole shape invariants have been implemented to investigate shape coexistence, and the distribution of possible shape-coexisting nuclei is consistent with results obtained in recent theoretical studies and available data. The present analysis has shown that, when based on a universal and consistent microscopic framework of nuclear density functionals, shape invariants provide distinct indicators and reliable predictions for the occurrence of low-energy coexisting shapes. This method is particularly useful for studies of shape coexistence in regions far from stability where few data are available.Comment: 13 pages, 3 figures, accepted for publication in Phys. Rev.

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs and distributed systems. Up to this point, no comparable effort has been made to parallelize the Elastic Net, despite its popularity in many high impact applications, including genetics, neuroscience and systems biology. The first contribution in this paper is of theoretical nature. We establish a tight link between two seemingly different algorithms and prove that Elastic Net regression can be reduced to SVM with squared hinge loss classification. Our second contribution is to derive a practical algorithm based on this reduction. The reduction enables us to utilize prior efforts in speeding up and parallelizing SVMs to obtain a highly optimized and parallel solver for the Elastic Net and Lasso. With a simple wrapper, consisting of only 11 lines of MATLAB code, we obtain an Elastic Net implementation that naturally utilizes GPU and multi-core CPUs. We demonstrate on twelve real world data sets, that our algorithm yields identical results as the popular (and highly optimized) glmnet implementation but is one or several orders of magnitude faster.Comment: 10 page

Quantum theory for mesoscopic electric circuits

A quantum theory for mesoscopic electric circuits in accord with the discreteness of electric charges is proposed. On the basis of the theory, Schr\"{o}dinger equation for the quantum LC-design and L-design is solved exactly. The uncertainty relation for electric charge and current is obtained and a minimum uncertainty state is solved. By introducing a gauge field, a formula for persistent current arising from magnetic flux is obtained from a new point of view.Comment: revtex, no figure

Impact of aerosol composition on cloud condensation nuclei activity

The impact of aerosol composition on cloud condensation nuclei (CCN) activity were analyzed in this study based on field experiments carried out at downtown Tianjin, China in September 2010. In the experiments, the CCN measurements were performed at supersaturation (SS) of 0.1%, 0.2% and 0.4% using a thermal-gradient diffusion chamber (DMT CCNC), whereas the aerosol size distribution and composition were simultaneously measured with a TSI SMPS and an Aerodyne Aerosol Mass Spectrometer (AMS), respectively. The results show that the influence of aerosol composition on CCN activity is notable under low SS (0.1%), and their influence decreased with increasing SS. For example, under SS of 0.1%, the CCN activity increases from 4.5±2.6% to 12.8±6.1% when organics fraction decrease from 30–40% to 10–20%. The rate of increase reached up to 184%. While under SS of 0.4%, the CCN activity increases only from 35.7±19.0% to 46.5±12.3% correspondingly. The calculated <i>N</i><sub>CCN</sub> based on the size-resolved activation ratio and aerosol number size distribution correlated well with observed <i>N</i><sub>CCN</sub> at high SS (0.4%), but this consistence decreased with the falling of SS. The slopes of linear fitted lines between calculated and observed <i>N</i><sub>CCN</sub> are 0.708, 0.947, and 0.995 at SS of 0.1%, 0.2% and 0.4% respectively. Moreover, the stand deviation (SD) of calculated <i>N</i><sub>CCN</sub> increased with the decreasing of SS. A case study of CCN closure analyses indicated that the calculated error of <i>N</i><sub>CCN</sub> could reach up to 34% at SS of 0.1% if aerosol composition were not included, and the calculated error decreased with the raising of SS. It is decreased to 9% at SS of 0.2%, and further decreased to 4% at SS of 0.4%

The Euler Number of Bloch States Manifold and the Quantum Phases in Gapped Fermionic Systems

We propose a topological Euler number to characterize nontrivial topological phases of gapped fermionic systems, which originates from the Gauss-Bonnet theorem on the Riemannian structure of Bloch states established by the real part of the quantum geometric tensor in momentum space. Meanwhile, the imaginary part of the geometric tensor corresponds to the Berry curvature which leads to the Chern number characterization. We discuss the topological numbers induced by the geometric tensor analytically in a general two-band model. As an example, we show that the zero-temperature phase diagram of a transverse field XY spin chain can be distinguished by the Euler characteristic number of the Bloch states manifold in a (1+1)-dimensional Bloch momentum space

Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network

© 2019 Elsevier B.V. Proteins often interact with each other and form protein complexes to carry out various biochemical activities. Knowledge of the interaction sites is helpful for understanding disease mechanisms and drug design. Accurate prediction of the interaction sites from protein sequences is still a challenging task and severe imbalance data also decreased the performance of computational methods. In this study, we propose to use a deep learning method for improving the imbalanced prediction of protein interaction sites. We develop a new simplified long short-term memory (SLSTM) network to implement a deep learning architecture (named DLPred). To deal with the imbalanced classification in the deep learning model, we explore three new ideas. First, our collection of the training data is to construct a set of protein sequences, instead of a set of just single residues, to retain the entire sequential completeness of each protein. Second, a new penalization factor is appended to the loss function such that the penalization to the non-interaction site loss can be effectively enhanced. Third, multi-task learning of interaction sites and residue solvent accessibility prediction are used for correcting the preference of the prediction model on the non-interaction sites. Our model is evaluated on three public datasets: Dset186, Dtestset72 and PDBtestset164. Compared with current state-of-the-art methods, DLPred is able to significantly improve the predictive accuracies and AUC values while improving the F-measure. The training dataset, test datasets, a standalone version of DLPred and online service are available at http://qianglab.scst.suda.edu.cn/dlp/

Co

Different loading rates of photocatalysts Co3O4/C3N4 were prepared by calcination method. Their photocatalytic performances were evaluated by the degradation of methyl blue under visible light irradiation. The results show that the introduction of Co3O4 significantly improves the optical absorption properties of C3N4, which is beneficial to the separation of photogenerated electrons and holes on the surface of catalyst. The prepared Co3O4/C3N4 for visible photocatalytic degradation of methyl blue has higher catalytic efficiency than that of pure C3N4 or pure Co3O4. The best cobalt loading rate was 30% when the concentration of methylene blue was 40 mg/L. Recycling rate of 30% Co3O4/C3N4 composite catalyst was studied. After 4 cycles, the degradation rate was only slightly decreased from 86.8% to 82.8%, indicating the catalyst with good photostability and repeatability.nbs

Glycerol-3-phosphate acyltransferase 3 (OsGPAT3) is required for anther development and male fertility in rice

Lipid molecules are key structural components of plant male reproductive organs, such as the anther and pollen. Although advances have been made in the understanding of acyl lipids in plant reproduction, the metabolic pathways of other lipid compounds, particularly glycerolipids, are not fully understood. Here we report that an endoplasmic reticulum-localized enzyme, Glycerol-3-Phosphate Acyltransferase 3 (OsGPAT3), plays an indispensable role in anther development and pollen formation in rice. OsGPAT3 is preferentially expressed in the tapetum and microspores of the anther. Compared with wild-type plants, the osgpat3 mutant displays smaller, pale yellow anthers with defective anther cuticle, degenerated pollen with defective exine, and abnormal tapetum development and degeneration. Anthers of the osgpat3 mutant have dramatic reductions of all aliphatic lipid contents. The defective cuticle and pollen phenotype coincide well with the down-regulation of sets of genes involved in lipid metabolism and regulation of anther development. Taking these findings together, this work reveals the indispensable role of a monocot-specific glycerol-3-phosphate acyltransferase in male reproduction in rice.Xiao Men, Jianxin Shi, Wanqi Liang, Qianfei Zhang, Gaibin Lian, Sheng Quan, Lu Zhu, Zhijing Luo, Mingjiao Chen, Dabing Zhan

Optimal kernel choice for domain adaption learning

© 2016 Elsevier Ltd. All rights reserved. In this paper, a kernel choice method is proposed for domain adaption, referred to as Optimal Kernel Choice Domain Adaption (OKCDA). It learns a robust classier and parameters associated with Multiple Kernel Learning side by side. Domain adaption kernel-based learning strategy has shown outstanding performance. It embeds two domains of different distributions, namely, the auxiliary and the target domains, into Hilbert Space, and exploits the labeled data from the source domain to train a robust kernel-based SVM classier for the target domain. We reduce the distributions mismatch by setting up a test statistic between the two domains based on the Maximum Mean Discrepancy (MMD) algorithm and minimize the Type II error, given an upper bound on error I. Simultaneously, we minimize the structural risk functional. In order to highlight the advantages of the proposed method, we tackle a text classification problem on 20 Newsgroups dataset and Email Spam dataset. The results demonstrate that our method exhibits outstanding performance.This work was supported in part by the National Natural Science Foundation of China under Grant 61370149, in part by the Fundamental Research Funds for the Central Universities (No. ZYGX2013J083), and in part by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (LXHG42DL)