Convergence of Calabi–Yau manifolds

In this paper, we study the convergence of Calabi–Yau manifolds under Kähler degeneration to orbifold singularities and complex degeneration to canonical singularities (including the conifold singularities), and the collapsing of a family of Calabi–Yau manifolds.     

单壁碳纳米管上毒性苯气体净化的分子模拟(英文)

采用巨正则系综蒙特卡罗(GCMC)方法研究了空气中微量苯组分在单臂碳纳米管(SWNTs)上的吸附净化.模拟表明,具有较大孔径的(20,20)纳米管比较适合吸附纯苯蒸汽,而对于移除空气中的毒性苯物质,苯的吸附选择性分别在(12,12)纳米管及4.0 MPa时和(18,18)纳米管及0.1 MPa时出现最小值和最大值.为了解释这一异常行为,我们进一步分析了N2-O2-C6H6混合物的局部密度分布、吸附分子构型和概率密度分布,发现(18,18)纳米管内外完全被苯分子占据,而对于(12,12)纳米管,由于存在更强的吸附质-吸附剂相互作用,空气分子更倾向于吸附在管与管之间的间隙.此外,吸附分子的空间有序参数表明大多数苯分子采取"平躺"在纳米管表面的定位,而线性的N2和O2分子则多数平行于孔轴方向.最后研究了温度和苯分子主体相浓度对分离效果的影响.我们发现较大孔中的选择性随着温度的增加比小孔下降更加明显.与此对比,主体相苯浓度对小孔中的选择性起到更加重要的作用.     

Adaptive lambda square dynamics simulation: An efficient conformational sampling method for biomolecules

A novel, efficient sampling method for biomolecules is proposed. The partial multicanonical molecular dynamics (McMD) was recently developed as a method that improved generalized ensemble (GE) methods to focus sampling only on a part of a system (GEPS); however, it was not tested well. We found that partial McMD did not work well for polylysine decapeptide and gave significantly worse sampling efficiency than a conventional GE. Herein, we elucidate the fundamental reason for this and propose a novel GEPS, adaptive lambda square dynamics (ALSD), which can resolve the problem faced when using partial McMD. We demonstrate that ALSD greatly increases the sampling efficiency over a conventional GE. We believe that ALSD is an effective method and is applicable to the conformational sampling of larger and more complicated biomolecule systems. © 2013 Wiley Periodicals, Inc.     

互信息诱导子空间集成偏最小二乘在近红外光谱定量校正中的应用

在集成框架下,提出了一种联合自助采样和基于互信息变量选择的子空间回归集成偏最小二乘算法MISEPLS.此算法的核心是通过训练集自助采样和随后计算互信息的方式来引入成员模型的差异性.由于互信息量小于一个特定阈值的变量被淘汰,每个成员模型在原始变量的一个子空间得到训练.模型融合考虑了简单平均和加权平均两种方式.通过两个近红外光谱定量校正实验,与建立单模型的全谱偏最小二乘算法(PLS)和基于互信息变量选择的偏最小二乘算法(MIPLS)进行了比较.结果表明,在不增加模型复杂度的情况下,MISEPLS能建立起更精确、更稳健的校正模型.     

On Generalized Algebraic Cone Metric Spaces and Fixed Point Theorems

In this paper, the author first introduces the concept of generalized algebraic cone metric spaces and some elementary results concerning generalized algebraic cone metric spaces. Next, by using these results, some new fixed point theorems on generalized (complete) algebraic cone metric spaces are proved and an example is given. As a consequence, the main results generalize the corresponding results in complete algebraic cone metric spaces and generalized complete metric spaces.     

Wahl’s conjecture holds in odd characteristics for symplectic and orthogonal Grassmannians

It is shown that the proof by Mehta and Parameswaran of Wahl’s conjecture for Grassmannians in positive odd characteristics also works for symplectic and orthogonal Grassmannians.      

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.