Systematic Review and Meta-Analysis on the Association Between Outpatient Statins Use and Infectious Disease-Related Mortality

Background: To update and refine systematic literature review on the association between outpatient statins use and mortality in patients with infectious disease. Materials and Methods: We searched articles published before September 31, 2012, on the association between statins and infectious disease-related mortality through electronic databases. Eligible articles were analyzed in Review Manager 5.1. We conducted stratification analysis by study design, infection types, clinical outcomes and study locations. Results: The pooled odds ratio (OR) for death (statins use vs. no use) across the 41 included studies was 0.71 (95% confidence interval: 0.64, 0.78). The corresponding pooled ORs were 0.58 (0.38, 0.90), 0.66 (0.57, 0.75), 0.71 (0.57, 0.89) and 0.83 (0.67, 1.04) for the case-control study, retrospective cohort studies, prospective cohort studies and RCTs; 0.40 (0.20, 0.78), 0.61 (0.41, 0.90), 0.69 (0.62, 0.78) and 0.86 (0.68, 1.09) for bacteremia, sepsis, pneumonia and other infections; 0.62 (0.534, 0.72), 0.68 (0.53, 0.89), 0.71 (0.61, 0.83) and 0.86 (0.70, 1.07) for 30-day, 90-day, in-hospital and long-term (>1 year) mortality, respectively. Conclusions: Outpatient statins use is associated with a lower risk of death in patients with infectious disease in observational studies, but in a less extent in clinical trials. This association also varies considerably by infection types and clinical outcomes

Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination

Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-of-the-art algorithms require the meta-training tasks to have a dense coverage on the task distribution and a great amount of data for each of them. In this paper, we propose MetaDreamer, a context-based Meta RL algorithm that requires less real training tasks and data by doing meta-imagination and MDP-imagination. We perform meta-imagination by interpolating on the learned latent context space with disentangled properties, as well as MDP-imagination through the generative world model where physical knowledge is added to plain VAE networks. Our experiments with various benchmarks show that MetaDreamer outperforms existing approaches in data efficiency and interpolated generalization

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.Comment: This is an extension of our long paper in ACM MM 201

Experimental verification on applying indirect inverse substructuring analysis to identify coupling dynamic stiffness of mechanical assembly via planar surface

To broaden the engineering application of inverse substructuring analysis, the mechanical assembly via planar surface is experimentally studied. Specifically, the first and the second schemes of indirect inverse substructuring analysis are applied to identify the coupling dynamic stiffness of the assembly. The experimental model of the assembly is designed, and the surface is then discretized equivalently into point-to-point connections for testing the frequency response functions (FRFs) involved in the schemes. Experimental results show that, applying both of the schemes are feasible for the identification, and the identified stiffnesses approach to be stable as the number of discretized points increases

Coupling dynamic stiffness identification of mechanical assembly with linear connection by the second indirect scheme of inverse substructuring analysis

A non-ideal connection of mechanical assembly with linear assembling interface is firstly considered in the coupling dynamic stiffness identification by applying the second scheme of indirect inverse substructuring analysis. The experimental model of the mechanical assembly is designed, and the interface is then discretized equivalently as ideal point-coupling for testing the frequency response functions (FRFs) involved in the scheme. As the results of the experimental study, applying the scheme is verified to be feasible for the stiffness identification of a mechanical assembly with linear connection, and the identified stiffness approaches to be stable with increase of the number of discretized points

Effective continuous model for surface states and thin films of three dimensional topological insulators

Two-dimensional effective continuous models are derived for the surface states and thin films of the three-dimensional topological insulator (3DTI). Starting from an effective model for 3DTI based on the first principles calculation [Zhang \emph{et al}, Nat. Phys. 5, 438 (2009)], we present solutions for both the surface states in a semi-infinite boundary condition and in the thin film with finite thickness. An effective continuous model was derived for surface states and the thin film 3DTI. The coupling between opposite topological surfaces and structure inversion asymmetry (SIA) give rise to gapped Dirac hyperbolas with Rashba-like splittings in energy spectrum. Besides, the SIA leads to asymmetric distributions of wavefunctions along the film growth direction, making some branches in the energy spectra much harder than others to be probed by light. These features agree well with the recent angle-resolved photoemission spectra of Bi$_{2}$Se $_{3}$ films grown on SiC substrate [Zhang et al, arXiv: 0911.3706]. More importantly, we use the effective model to fit the experimental data and determine the model parameters. The result indicates that the thin film Bi$_{2}$Se$_{3}$ lies in quantum spin Hall region based on the calculation of the Chern number and the $Z_{2}$ invariant. In addition, strong SIA always intends to destroy the quantum spin Hall state.Comment: 12 pages, 7 figures, references are update

Spin-orbit scattering in quantum diffusion of massive Dirac fermions

Effect of spin-orbit scattering on quantum diffusive transport of two-dimensional massive Dirac fermions is studied by the diagrammatic technique. The quantum diffusion of massive Dirac fermions can be viewed as a singlet Cooperon in the massless limit and a triplet Cooperon in the large-mass limit. The spin-orbit scattering behaves like random magnetic fields only to the triplet Cooperon, and suppresses the weak localization of Dirac fermions in the large-mass regime. This behavior suggests an experiment to detect the weak localization of bulk subbands in topological insulator thin films, in which a narrowing of the cusp of the negative magnetoconductivity is expected after doping heavy-element impurities. Finally, a detailed comparison between the conventional two-dimensional electrons and Dirac fermions is presented for impurities of orthogonal, symplectic, and unitary symmetries.Comment: 5 pages, 3 figures, 2 tables. To be submitted, comments are welcom