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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 BiSe 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 BiSe lies in
quantum spin Hall region based on the calculation of the Chern number and the
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
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