Horn formulas are a subclass of CNF expressions, where every clause contains at most one unnegated variable. This class is tractable in the sense that many problems that are hard for CNF expressions in general are polynomially solvable for Horn formulas (such as satisfiability and equivalence). It is partly for this reason that Horn formulas are of basic importance in artificial intelligence an...

In this paper, we study Riesz basis property and stability for a nonuniform thermoelastic system with Dirichlet-Dirichlet boundary condition, where the heat subsystem is considered as control to whole coupled system. By means of matrix operator pencil method, obtain asymptotic expressions eigenpairs, which are exactly coincident constant coefficients case. We then show that there exists sequenc...

In HMM-based pattern recognition, the structure of the HMM is often predetermined according to some prior knowledge. In the recognition process, we usually make our judgment based on the maximum likelihood of the HMM, without considering the time-varying property of state-level variables, which unfortunately may lead to incorrect results. In this paper, we analyze the property of state-level va...

We show how to mechanise equational proofs about higher-order languages by using the primitive proof principles of first-order abstract syntax over one-sorted variable names. We illustrate the method here by proving (in Isabelle/HOL) a technical property which makes the method widely applicable for the λ-calculus: the residual theory of β is renaming-free up-to an initiality condition akin to t...

Little and An (2004, Statistica Sinica 14, 949-968) proposed a penalized spline of propensity prediction (PSPP) method of imputation of missing values that yields robust model-based inference under the missing at random assumption. The propensity score for a missing variable is estimated and a regression model is fitted that includes the spline of the estimated logit propensity score as a covar...

The C-statistic, measuring the rank concordance between predictors and outcomes, has become a standard metric of predictive accuracy and is therefore a natural criterion for variable screening and selection. However, as the C-statistic is a step function, its optimization requires brute-force search, prohibiting its direct usage in the presence of high-dimensional predictors. We develop a smoot...

A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in Fan and Li (2001a). It has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance. Such a property is called an oracle property. The proposed procedures were illustrated in the context of linear regression, rob...