This paper introduces a constraint logic programming (CLP) language cu-Prolog as an implementation framework for constraint-based natural language processing. Compared to other CLP languages, cu-Prolog has several unique features. Most CLP languages take algebraic equations or inequations as constraints. cu-Prolog, on the other hand, takes Prolog atomic formulas in terms of user-defined predicates. cu-Prolog, thus, can describe symbolic and combinatorial constraints occurring in the constraint-based grammar formalisms. As a constraint solver, cu-Prolog uses the unfold/fold transformation, which is well known as a program transformation technique, dynamically with some heuristics. To treat the information partiality described with feature structures, cu-Prolog uses PST (Partially Specified Term) as its data structure. Sections 1 and 2 give an introduction to the constraint-based grammar formalisms on which this paper is based and the outline of cu-Prolog is explained in Sect. 3 with implementation issues described in Sect. 4. Section 5 illustrates its linguistic application to disjunctive feature structure (DFS) and parsing constraint-based grammar formalisms such as Japanese Phrase Structure Grammar (JPSG). In either application, a disambiguation process is realized by transforming constraints, which gives a picture of constraint-based NLP.

A novel via chain structure for failure analysis at 65 nm-node fixing OPC using inner and outer via chain dummy patterns has been proposed. The inner dummy is necessary to localize failure site in 200 nm pitch via chain using an optical beam induced resistance change method. The outer dummy protects via chain pattern from local flare and optical proximity effects. Using this test structure, we can identify the failure point in the 1.2 k and 15 k via chain fabricated by Cu/low-k single damascene process. This test structure is beneficial in the application to the 65 nm-node technologies and beyond.

This paper studies a privacy-preserving decision tree learning protocol (PPDT) for vertically partitioned datasets. In vertically partitioned datasets, a single class (target) attribute is shared by both parities or carefully treated by either party in existing studies. The proposed scheme allows both parties to have independent class attributes in a secure way and to combine multiple class attributes in arbitrary boolean function, which gives parties some flexibility in data-mining. Our proposed PPDT protocol reduces the CPU-intensive computation of logarithms by approximating with a piecewise linear function defined by light-weight fundamental operations of addition and constant multiplication so that information gain for attributes can be evaluated in a secure function evaluation scheme. Using the UCI Machine Learning dataset and a synthesized dataset, the proposed protocol is evaluated in terms of its accuracy and the sizes of trees*.