We present results for the hybrid meson spectrum produced by gluonic excitations in full QCD using Wilson fermions. For the spin-exotic mesons with J P C = 1 −+ , 0 +− , and 2 +− we find the lightest state to be 1 −+ with a mass of 1.9(2) GeV. Results obtained for orbitally excited mesons are also presented.

To get X-rays out of a suitable vacuum tube, it needs to be excited by an electric discharge at a tension of hundreds of thousands of volts. Such a discharge is furnished by the induction coil and by the static induction or " influence" machine, associated in its most elegant and effective form with the name of Mr. Wimshurst. In spite of the obvious advantages of the latter type of machine over...

This paper presents a novel hybrid system, which combines rule-based machine translation (RBMT) with phrase-based statistical machine translation (SMT), to translate Chinese patent texts into English. The hybrid architecture is basically guided by the RBMT engine which processes source language parsing and transformation, generating proper syntactic trees for the target language. In the generat...

Lattice QCD determinations appropriate to hadron spectroscopy are reviewed with emphasis on the glueball and hybrid meson states in the quenched approximation. Hybrids are discussed for heavy and for light quarks. The effects of sea quarks (unquenching) are explored. Heavy-light systems are presented particularly excited B mesons.

The main goal of the OpenMT project is the development of open source machine translation architectures based on hybrid models and advanced syntactic–semantic processors. These architectures combine the three main Machine Translation (MT) frameworks, Rule-based (RBMT), Statistical (SMT) and Example–based (EBMT), into hybrid systems. Defined architectures and results will be open source, allow f...

We present a hybrid machine learning approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rule...