We have analyzed the clustering of ~ 90,000 narrow-line AGN drawn from the
Data Release 4 (DR4) of the SDSS. We compute the cross-correlation between AGN
and a reference sample of galaxies, and compare this to results for control
samples of inactive galaxies matched simultaneously in redshift,stellar
mass,concentration, velocity dispersion and the 4000A break strength. We also
compare near-neighbour counts around AGN and around the control galaxies. On
scales larger than a few Mpc, AGN have almost the same clustering amplitude as
the control sample. This demonstrates that AGN host galaxies and inactive
galaxies populate dark matter halos of similar mass.On scales between 100kpc
and 1Mpc,AGN are clustered more weakly than the control galaxies. We use mock
catalogues constructed from high-resolution N-body simulations to interpret
this anti-bias, showing that the observed effect is easily understood if AGN
are preferentially located at the centres of their dark matter halos. On scales
less than 70 kpc, AGN cluster marginally more strongly than the control sample,
but the effect is weak. When compared to the control sample, we find that only
one in a hundred AGN has an extra neighbour within a radius of 70 kpc. This
excess increases as a function of the accretion rate onto the black hole, but
it does not rise above the few percent level. Although interactions between
galaxies may be responsible for triggering nuclear activity in a minority of
nearby AGN, some other mechanism is required to explain the activity seen in
the majority of the objects in our sample. (abridged)Comment: 14 pages, 11 figures, accepted to MNRAS, Fig.2 redrawn and text
slightly change
In practical applications of pattern recognition, there are often different features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with different features are viewed as a general problem in various application areas of pattern recognition. In this paper, a systematic investigation has been made and possible solutions are classified into three frameworks, i.e. linear opinion pools, winner-take-all and evidential reasoning. For combining multiple classifiers with different features, a novel method is presented in the framework of linear opinion pools and a modified training algorithm for associative switch is also proposed in the framework of winner-take-all. In the framework of evidential reasoning, several typical methods are briefly reviewed for use. All aforementioned methods have already been applied to text-independent speaker identification. The simulations show that results yielded by the methods described in this paper are better than not only the individual classifiers' but also ones obtained by combining multiple classifiers with the same feature. It indicates that the use of combining multiple classifiers with different features is an effective way to attack the problem of text-independent speaker identification.
The differential impact of social capital among employees in strategic and support roles has received far less attention than that of human capital in talent management literature. Building on network closure theory and differentiated workforce theory, we examine the effect of strategic and support teams’ experience ties on team performance while controlling for human capital using current Moneyball‐inspired metrics for workforce quality. Using an 111‐year longitudinal data set of 15,837 Major League Baseball players from all 30 teams and 3,475,778 experience ties, we find that after accounting for the effect of team quality, managerial stability and reputation, and era effects, organizational experience ties and subsequent team performance have an inverted U‐shaped relationship for strategic roles and a U‐shaped relationship for support roles. Competitor experience ties have an inverted U‐shaped relationship on performance for strategic roles, yet the hypothesized U‐shaped relationship showed differences for different competency areas among support roles. This study highlights the value of social capital to team performance and the importance of differentiating human resource management (HRM) practices for strategic and support roles in 20 different competency areas. It also showcases how workforce analytics with big data can be applied to HRM and have value added impact on workforce and firm strategy execution.
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