Three-dimensional structures of the spatiotemporal nonlinear Schrödinger equation with power-law nonlinearity in PT-symmetric potentials

The spatiotemporal nonlinear Schrödinger equation with power-law nonlinearity in PT-symmetric potentials is investigated, and two families of analytical three-dimensional spatiotemporal structure solutions are obtained. The stability of these solutions is tested by the linear stability analysis and the direct numerical simulation. Results indicate that solutions are stable below some thresholds for the imaginary part of PT-symmetric potentials in the self-focusing medium, while they are always unstable for all parameters in the self-defocusing medium. Moreover, some dynamical properties of these solutions are discussed, such as the phase switch, power and transverse power-flow density. The span of phase switch gradually enlarges with the decrease of the competing parameter k in PT-symmetric potentials. The power and power-flow density are all positive, which implies that the power flow and exchange from the gain toward the loss domains in the PT cell.Funded by the National Natural Science Foundation of China (Grant No. 11375007), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY13F050006)

Domain Adaptation and Image Classification via Deep Conditional Adaptation Network

Unsupervised domain adaptation aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Marginal distribution alignment of feature spaces is widely used to reduce the domain discrepancy between the source and target domains. However, it assumes that the source and target domains share the same label distribution, which limits their application scope. In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same. In this scenario, marginal distribution alignment-based methods will be vulnerable to negative transfer. To address this issue, we propose a novel unsupervised domain adaptation method, Deep Conditional Adaptation Network (DCAN), based on conditional distribution alignment of feature spaces. To be specific, we reduce the domain discrepancy by minimizing the Conditional Maximum Mean Discrepancy between the conditional distributions of deep features on the source and target domains, and extract the discriminant information from target domain by maximizing the mutual information between samples and the prediction labels. In addition, DCAN can be used to address a special scenario, Partial unsupervised domain adaptation, where the target domain category is a subset of the source domain category. Experiments on both unsupervised domain adaptation and Partial unsupervised domain adaptation show that DCAN achieves superior classification performance over state-of-the-art methods. In particular, DCAN achieves great improvement in the tasks with large difference in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on Office-Home and 4.5\% in Partial UDA tasks on Office-Home)

Unsupervised Domain Adaptation via Discriminative Manifold Propagation

Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy made a significant breakthrough in the adaptability of features, there are two issues to be further studied. First, hard-assigned pseudo labels on the target domain are arbitrary and error-prone, and direct application of them may destroy the intrinsic data structure. Second, batch-wise training of deep learning limits the characterization of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability simultaneously. For the first issue, this framework establishes a probabilistic discriminant criterion on the target domain via soft labels. Based on pre-built prototypes, this criterion is extended to a global approximation scheme for the second issue. Manifold metric alignment is adopted to be compatible with the embedding space. The theoretical error bounds of different alignment metrics are derived for constructive guidance. The proposed method can be used to tackle a series of variants of domain adaptation problems, including both vanilla and partial settings. Extensive experiments have been conducted to investigate the method and a comparative study shows the superiority of the discriminative manifold learning framework.Comment: To be published in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Classic swine fever virus NS2 protein leads to the induction of cell cycle arrest at S-phase and endoplasmic reticulum stress

<p>Abstract</p> <p>Background</p> <p>Classical swine fever (CSF) caused by virulent strains of Classical swine fever virus (CSFV) is a haemorrhagic disease of pigs, characterized by disseminated intravascular coagulation, thrombocytopoenia and immunosuppression, and the swine endothelial vascular cell is one of the CSFV target cells. In this report, we investigated the previously unknown subcellular localization and function of CSFV NS2 protein by examining its effects on cell growth and cell cycle progression.</p> <p>Results</p> <p>Stable swine umbilical vein endothelial cell line (SUVEC) expressing CSFV NS2 were established and showed that the protein localized to the endoplasmic reticulum (ER). Cellular analysis revealed that replication of NS2-expressing cell lines was inhibited by 20-30% due to cell cycle arrest at S-phase. The NS2 protein also induced ER stress and activated the nuclear transcription factor kappa B (NF-κB). A significant increase in cyclin A transcriptional levels was observed in NS2-expressing cells but was accompanied by a concomitant increase in the proteasomal degradation of cyclin A protein. Therefore, the induction of cell cycle arrest at S-phase by CSFV NS2 protein is associated with increased turnover of cyclin A protein rather than the down-regulation of cyclin A transcription.</p> <p>Conclusions</p> <p>All the data suggest that CSFV NS2 protein modulate the cellular growth and cell cycle progression through inducing the S-phase arrest and provide a cellular environment that is advantageous for viral replication. These findings provide novel information on the function of the poorly characterized CSFV NS2 protein.</p

Prevalence of insomnia symptoms and their associated factors in patients treated in outpatient clinics of four general hospitals in Guangzhou, China

Background: Data on the prevalence of insomnia symptoms in medical outpatient clinics in China are lacking. This study examined the prevalence of insomnia symptoms and their socio-demographic correlates in patients treated at medical outpatient clinics affiliated with four general hospitals in Guangzhou, a large metropolis in southern China. Method: A total of 4399 patients were consecutively invited to participate in the study. Data on insomnia and its socio-demographic correlates were collected with standardized questionnaires. Results: The prevalence of any type of insomnia symptoms was 22.1% (95% confidence interval (CI): 20.9–23.3%); the prevalence of difficulty initiating sleep was 14.3%, difficulty maintaining sleep was 16.2%, and early morning awakening was 12.4%. Only 17.5% of the patients suffering from insomnia received sleeping pills. Multiple logistic regression analysis revealed that male gender, education level, rural residence, and being unemployed or retired were negatively associated with insomnia symptoms, while lacking health insurance, older age and more severe depressive symptoms were positively associated with insomnia symptoms. Conclusions: Insomnia symptoms are common in patients attending medical outpatient clinics in Guangzhou. Increasing awareness of sleep hygiene measures, regular screening and psychosocial and pharmacological interventions for insomnia are needed in China. Trial registration: ChiCTR-INR-16008066. Registered 8 March 2016

Bis{μ-2-[(2-oxidobenzyl­idene)amino­meth­yl]phenolato-κ3 O,N,O′}bis­[(pyridine-κN)zinc(II)]

In the title centrosymmetric zinc(II) complex, [Zn2(C4H13NO2)2(C6H5N)2], each ZnII atom is coordinated by two 2-[(2-oxidobenzyl­idene)amino­meth­yl]phenolate (L) ligands and one pyridine (py) mol­ecule in a distorted trigonal-bipyramidal geometry. Each L ligand behaves as a tridentate ligand and provides a phenolate oxygen bridge which links the two ZnII atoms. The ZnL(py) units are linked by π–π inter­actions between adjacent pyridine mol­ecules, with a centroid–centroid distance of 3.724 Å, resulting in a two-dimensional structure