A Lower Bound on the Second Fiber Coefficient of the Fiber Cones

Let (R, 𝔪) be a Cohen–Macaulay local ring of dimension d > 0, I an 𝔪-primary ideal of R and K an ideal containing I. When depth G(I) ≥ d ? 1 and r(I | K) < ∞, we present a lower bound on the second fiber coefficient of the fiber cones, and also provide a characterization, in terms of f ₂(I, K), of the condition depth F _K (I) ≥ d ? 1.     

船摇数据实时滤波与预报的时序法

作为船摇数据建立一个时序时域模型-长自回归模型,在此模型基础上给出了船摇数据的实时滤波和预报的方法,并从均方误差、预报误差、残差序列的相关性、拟合方差、仿真计算等多方面考察了滤波及预报效果。AR（p)模型的系数估计采用最小二乘递推方法,用较少的运算量和存贮量,得到了较高的估值精度。该方法是一种非常值得推荐的船摇数据实时处理方法。     

Functional Additive Mixed Models

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, for example, spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well, and also scales to larger datasets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.     

A Hessian Regularized Nonlinear Time Series Model

We introduce a nonparametric nonlinear time series model. The novel idea is to fit a model via penalization, where the penalty term is an unbiased estimator of the integrated Hessian of the underlying function. The underlying model assumption is very general: it has Hessian almost everywhere in its domain. Numerical experiments demonstrate that our model has better predictive power: if the underlying model complies with an existing parametric/semiparametric form (e.g., a threshold autoregressive model (TAR), an additive autoregressive model (AAR), or a functional coefficient autoregressive model (FAR)), our model performs comparably; if the underlying model does not comply with any preexisting form, our model outperforms in nearly all simulations. We name our model a Hessian regularized nonlinear model for time series (HRM). We conjecture on theoretical properties and use simulations to verify. Our method can be viewed as a way to generalize splines to high dimensions (when the number of variates is more than three), under which an analogous analytical derivation cannot work due to the curse of dimensionality. Supplemental materials are provided, and will help readers reproduce all results in the article.     

The learning rate of l 2-coefficient regularized classification with strong loss

In the present paper, we give an investigation on the learning rate of l2-coefficient regularized classification with strong loss and the data dependent kernel functional spaces. The results show that the learning rate is influenced by the strong convexity.     

Subspace Inclusion Graph of a Vector Space

In this paper, the authors introduce a graph structure, called subspace inclusion graph ?n(𝕍) on a finite dimensional vector space 𝕍 where the vertex set is the collection of nontrivial proper subspaces of a vector space and two vertices are adjacent if one is contained in other. The diameter, girth, clique number, and chromatic number of ?n(𝕍) are studied. It is shown that two subspace inclusion graphs are isomorphic if and only if the base vector spaces are isomorphic. Finally, some properties of subspace inclusion graph are studied when the base field is finite.     

一种新的计算Timoshenko梁截面剪切系数的方法

Timoshenko梁理论中考虑了截面剪切变形的影响,推导了一种新的计算剪切系数的方法,首先采用悬臂梁纯弯曲变形条件下截面剪应力分布的精确解,基于能量原理得到了各种梁截面剪切系数新的表达式,然后推导了弯扭耦合变形条件下截面剪应力分布的精确解,进一步获得了该条件下截面的剪切系数.结果表明,悬臂梁端面作用力偏离截面的弯曲中心将使剪切系数变小,通过与Cowper计算结果的对比发现结果偏小,其原因是Cowper没有考虑与外力垂直的剪应力的影响,因此新的计算结果更优越.     

一个带零齐次核的Hilbert型积分不等式

引入独立参数及共轭指数,应用权函数方法和实分析技巧,建立一个零齐次核的带最佳常数的Hilbert型积分不等式,并考虑其等价形式及其逆式.     

Smoothness of Densities of Generalized Locally Non-Degenerate Wiener Functionals

Several criteria for existence of smooth densities of Wiener functionals are known in the framework of Malliavin calculus. In this article, we introduce the notion of generalized locally non-degenerate Wiener functionals and prove that they possess smooth densities. The result presented here unifies the earlier works by Shigekawa and Florit-Nualart. As an application, we prove that the law of the strong solution to a stochastic differential equation driven by Brownian motion admits a smooth density without an assumption of Lipschitz continuity for dispersion coefficients.