Comparison of advanced gravitational-wave detectors

We compare two advanced designs for gravitational-wave antennas in terms of their ability to detect two possible gravitational wave sources. Spherical, resonant mass antennas and interferometers incorporating resonant sideband extraction (RSE) were modeled using experimentally measurable parameters. The signal-to-noise ratio of each detector for a binary neutron star system and a rapidly rotating stellar core were calculated. For a range of plausible parameters we found that the advanced LIGO interferometer incorporating RSE gave higher signal-to-noise ratios than a spherical detector resonant at the same frequency for both sources. Spheres were found to be sensitive to these sources at distances beyond our galaxy. Interferometers were sensitive to these sources at far enough distances that several events per year would be expected

An Approach to the POS Tagging Problem Using Genetic Algorithms

The automatic part-of-speech tagging is the process of automatically assigning to the words of a text a part-of-speech (POS) tag. The words of a language are grouped into grammatical categories that represent the function that they might have in a sentence. These grammatical classes (or categories) are usually called part-of-speech. However, in most languages, there are a large number of words that can be used in different ways, thus having more than one possible part-of-speech. To choose the right tag for a particular word, a POS tagger must consider the surrounding words’ part-of-speeches. The neighboring words could also have more than one possible way to be tagged. This means that, in order to solve the problem, we need a method to disambiguate a word’s possible tags set. In this work, we modeled the part-of-speech tagging problem as a combinatorial optimization problem, which we solve using a genetic algorithm. The search for the best combinatorial solution is guided by a set of disambiguation rules that we first discovered using a classification algorithm, that also includes a genetic algorithm. Using rules to disambiguate the tagging, we were able to generalize the context information present on the training tables adopted by approaches based on probabilistic data. We were also able to incorporate other type of information that helps to identify a word’s grammatical class. The results obtained on two different corpora are amongst the best ones published