The Experts below are selected from a list of 183 Experts worldwide ranked by ideXlab platform
T. R. Bradley - One of the best experts on this subject based on the ideXlab platform.
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A composite Hii region luminosity function in Hof unprecedented Statistical Weight
2020Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of Hii region populations in disk galaxies yield important clues t o the physics of massive star formation. Aims. We present a set of Hii region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. Hii region luminosity functions are derived for individual gal axies which, after photometric calibration, are summed to form a total luminosity function comprising 17,797 Hii regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for
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a composite h ii region luminosity function in hα of unprecedented Statistical Weight
Astronomy and Astrophysics, 2006Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of H region populations in disk galaxies yield important clues to the physics of massive star formation. Aims. We present a set of H region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. H region luminosity functions are derived for individual galaxies which, after photometric calibration, are summed to form a total luminosity function comprising 17 797 H regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for individual galaxies, and occurs at a luminosity of log L = 38.6 ± 0. 1( L in erg s −1 ) which has been termed the Stromgren luminosity. A steep fall off in the luminosity function above log L = 40 is also noted, and is related to an upper limit to the luminosities of underlying massive stellar clusters. Detailed data are presented for the individual sample galaxies. Conclusions. The luminosity functions of H regions in spiral galaxies show a two slope power law behaviour, with a significantly steeper slope for the high luminosity branch. This can be modelled by assuming that the high luminosity regions are density bounded, though the scenario is complicated by the inhomogeneity of the ionized interstellar medium. The break, irrespective of its origin, is of potential use as a distance indicator for disc galaxies.
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a composite hii region luminosity function in h alpha of unprecedented Statistical Weight
arXiv: Astrophysics, 2006Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of HII region populations in disk galaxies yield important clues to the physics of massive star formation. Aims. We present a set of HII region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. HII region luminosity functions are derived for individual galaxies which, after photometric calibration, are summed to form a total luminosity function comprising 17,797 HII regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for individual galaxies, and occurs at a luminosity of log L = 38.6\pm0.1 (L in erg/s) which has been termed the Stromgren luminosity. A steep fall off in the luminosity function above log L = 40 is also noted, and is related to an upper limit to the luminosities of underlying massive stellar clusters. Detailed data are presented for the individual sample galaxies. Conclusions. The luminosity functions of HII regions in spiral galaxies show a two slope power law behaviour, with a significantly steeper slope for the high luminosity branch. This can be modelled by assuming that the high luminosity regions are density bounded, though the scenario is complicated by the inhomogeneity of the ionized interstellar medium. The break, irrespective of its origin, is of potential use as a distance indicator for disc galaxies.
James R Curran - One of the best experts on this subject based on the ideXlab platform.
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random indexing using Statistical Weight functions
Empirical Methods in Natural Language Processing, 2006Co-Authors: James Gorman, James R CurranAbstract:Random Indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. We present experiments showing Random Indexing to be poor at handling large volumes of data and evaluate the use of Weighting functions for improving the performance of Random Indexing. We find that Random Index is robust for small data sets, but performance degrades because of the influence high frequency attributes in large data sets. The use of appropriate Weight functions improves this significantly.
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EMNLP - Random Indexing using Statistical Weight Functions
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06, 2006Co-Authors: James Gorman, James R CurranAbstract:Random Indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. We present experiments showing Random Indexing to be poor at handling large volumes of data and evaluate the use of Weighting functions for improving the performance of Random Indexing. We find that Random Index is robust for small data sets, but performance degrades because of the influence high frequency attributes in large data sets. The use of appropriate Weight functions improves this significantly.
Yin Liang - One of the best experts on this subject based on the ideXlab platform.
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ISKE - Chinese keyword extraction based on Weighted complex network
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017Co-Authors: Yin LiangAbstract:Aiming at the problem of low precision of keyword extraction in traditional complex network method, we propose a keyword extraction method based on an improved Weighted complex network, called IWCN algorithm. First, based on the word semantic similarity, we construct a complex network to obtain semantic Weight of words. Next, the Statistical Weight of words is obtained by the introduction of term frequency (TF) and inverse document frequency (IDF). Finally, we combine semantic and Statistical Weights of words to get keywords. Comparing to traditional complex network approach, the proposed method can avoid the deviations and thus improves extraction accuracy. Simulation results shows that the proposed method achieves higher precision and recall.
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Chinese keyword extraction based on Weighted complex network
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017Co-Authors: Yin LiangAbstract:Aiming at the problem of low precision of keyword extraction in traditional complex network method, we propose a keyword extraction method based on an improved Weighted complex network, called IWCN algorithm. First, based on the word semantic similarity, we construct a complex network to obtain semantic Weight of words. Next, the Statistical Weight of words is obtained by the introduction of term frequency (TF) and inverse document frequency (IDF). Finally, we combine semantic and Statistical Weights of words to get keywords. Comparing to traditional complex network approach, the proposed method can avoid the deviations and thus improves extraction accuracy. Simulation results shows that the proposed method achieves higher precision and recall.
S. L. Folkes - One of the best experts on this subject based on the ideXlab platform.
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A composite Hii region luminosity function in Hof unprecedented Statistical Weight
2020Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of Hii region populations in disk galaxies yield important clues t o the physics of massive star formation. Aims. We present a set of Hii region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. Hii region luminosity functions are derived for individual gal axies which, after photometric calibration, are summed to form a total luminosity function comprising 17,797 Hii regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for
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a composite h ii region luminosity function in hα of unprecedented Statistical Weight
Astronomy and Astrophysics, 2006Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of H region populations in disk galaxies yield important clues to the physics of massive star formation. Aims. We present a set of H region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. H region luminosity functions are derived for individual galaxies which, after photometric calibration, are summed to form a total luminosity function comprising 17 797 H regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for individual galaxies, and occurs at a luminosity of log L = 38.6 ± 0. 1( L in erg s −1 ) which has been termed the Stromgren luminosity. A steep fall off in the luminosity function above log L = 40 is also noted, and is related to an upper limit to the luminosities of underlying massive stellar clusters. Detailed data are presented for the individual sample galaxies. Conclusions. The luminosity functions of H regions in spiral galaxies show a two slope power law behaviour, with a significantly steeper slope for the high luminosity branch. This can be modelled by assuming that the high luminosity regions are density bounded, though the scenario is complicated by the inhomogeneity of the ionized interstellar medium. The break, irrespective of its origin, is of potential use as a distance indicator for disc galaxies.
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a composite hii region luminosity function in h alpha of unprecedented Statistical Weight
arXiv: Astrophysics, 2006Co-Authors: T. R. Bradley, J. H. Knapen, John E. Beckman, S. L. FolkesAbstract:Context. Statistical properties of HII region populations in disk galaxies yield important clues to the physics of massive star formation. Aims. We present a set of HII region catalogues and luminosity functions for a sample of 56 spiral galaxies in order to derive the most general form of their luminosity function. Methods. HII region luminosity functions are derived for individual galaxies which, after photometric calibration, are summed to form a total luminosity function comprising 17,797 HII regions from 53 galaxies. Results. The total luminosity function, above its lower limit of completeness, is clearly best fitted by a double power law with a significantly steeper slope for the high luminosity portion of the function. This change of slope has been reported in the literature for individual galaxies, and occurs at a luminosity of log L = 38.6\pm0.1 (L in erg/s) which has been termed the Stromgren luminosity. A steep fall off in the luminosity function above log L = 40 is also noted, and is related to an upper limit to the luminosities of underlying massive stellar clusters. Detailed data are presented for the individual sample galaxies. Conclusions. The luminosity functions of HII regions in spiral galaxies show a two slope power law behaviour, with a significantly steeper slope for the high luminosity branch. This can be modelled by assuming that the high luminosity regions are density bounded, though the scenario is complicated by the inhomogeneity of the ionized interstellar medium. The break, irrespective of its origin, is of potential use as a distance indicator for disc galaxies.
James Gorman - One of the best experts on this subject based on the ideXlab platform.
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random indexing using Statistical Weight functions
Empirical Methods in Natural Language Processing, 2006Co-Authors: James Gorman, James R CurranAbstract:Random Indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. We present experiments showing Random Indexing to be poor at handling large volumes of data and evaluate the use of Weighting functions for improving the performance of Random Indexing. We find that Random Index is robust for small data sets, but performance degrades because of the influence high frequency attributes in large data sets. The use of appropriate Weight functions improves this significantly.
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EMNLP - Random Indexing using Statistical Weight Functions
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06, 2006Co-Authors: James Gorman, James R CurranAbstract:Random Indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. We present experiments showing Random Indexing to be poor at handling large volumes of data and evaluate the use of Weighting functions for improving the performance of Random Indexing. We find that Random Index is robust for small data sets, but performance degrades because of the influence high frequency attributes in large data sets. The use of appropriate Weight functions improves this significantly.
