Incomplete Augmented Lagrangian Preconditioner for Steady Incompressible Navier-Stokes Equations

An incomplete augmented Lagrangian preconditioner, for the steady incompressible Navier-Stokes equations discretized by stable finite elements, is proposed. The eigenvalues of the preconditioned matrix are analyzed. Numerical experiments show that the incomplete augmented Lagrangian-based preconditioner proposed is very robust and performs quite well by the Picard linearization or the Newton linearization over a wide range of values of the viscosity on both uniform and stretched grids

Exploited by complexity

Due to their complex features, structured financial products can hurt the average investor. Are certain investors particularly vulnerable? Using account-level transaction data of retail structured funds, we show that the rich (sophisticated) benefit from complexity at the expense of the poor (naive). The poor-to-rich wealth transfer that results from trading structured funds is substantially greater than from trading simple, nonstructured funds. In an event study, we further confirm that part of this wealth transfer can be directly attributed to investors' differing responses to complexity. In particular, when a market crash triggers funds into a restructuring process and their prices are expected to shrink by half on a given day, the poor and naive subset of investors fails to respond effectively

Computational evaluation of TIS annotation for prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.</p> <p>Results</p> <p>Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, <it>i.e</it>. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.</p> <p>Conclusion</p> <p>Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.</p

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

B meson rare decays in the TNMSSM

We investigate the two loop electroweak corrections to B meson rare decays $\bar B\rightarrow X_s\gamma$ and $B_s^0\rightarrow \mu^+\mu^-$ in the minimal supersymmetry standard model (MSSM) extension with two triplets and one singlet (TNMSSM). The new particle contents and interactions in the TNMSSM can affect the theoretical predictions of the branching ratios ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$, and the corrections from two loop diagrams to the process $\bar B\rightarrow X_s\gamma$ can reach around $4\%$. Considering the latest experimental measurements, the numerical results of ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$ in the TNMSSM are presented and analyzed. It is found that the results in the TNMSSM can fit the updated experimental data well and the new parameters $T_{\lambda},\;\kappa,\;\lambda$ affect the theoretical predictions of ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$ obviously

Nonideal MHD Simulation of HL Tau Disk: Formation of Rings

Recent high-resolution observations unveil ring structures in circumstellar disks. The origin of these rings has been widely investigated under various theoretical scenarios. In this work we perform global 3D nonideal MHD simulations including effects from both ohmic resistivity and ambipolar diffusion (AD) to model the HL Tau disk. The nonideal MHD diffusion profiles are calculated based on the global dust evolution calculation including sintering effects. Disk ionization structure changes dramatically across the snow line due to the change of dust size distribution close to the snow line of major volatiles. We find that accretion is mainly driven by disk wind. Gaps and rings can be quickly produced from different accretion rates across the snow line. Furthermore, AD leads to highly preferential accretion at the midplane, followed by magnetic reconnection. This results in a local zone of decretion that drains the mass in the field reconnection area, which leaves a gap and an adjacent ring just outside of it. Overall, under favorable conditions, both snow lines and nonideal MHD effects can lead to gaseous gaps and rings in protoplanetary disks

A splitting preconditioner for the incompressible navier–stokes equations

In this paper, a splitting preconditioner based on the relaxed dimensional factorization (RDF) preconditioner and the modified augmented Lagrangian (MAL) preconditioner for the incompressible Navier–Stokes equations is presented. The preconditioned matrix is analyzed, and similar results arising from the RDF and the MAL preconditioners are obtained. The corresponding details of the spectrum analysis are given. Finally, we compare the three preconditioners and numerical experiments are implemented by using the IFISS package