Quark charge balance function and hadronization effects in relativistic heavy ion collisions

We calculate the charge balance function of the bulk quark system before hadronization and those for the directly produced and the final hadron system in high energy heavy ion collisions. We use the covariance coefficient to describe the strength of the correlation between the momentum of the quark and that of the anti-quark if they are produced in a pair and fix the parameter by comparing the results for hadrons with the available data. We study the hadronization effects and decay contributions by comparing the results for hadrons with those for the bulk quark system. Our results show that while hadronization via quark combination mechanism slightly increases the width of the charge balance functions, it preserves the main features of these functions such as the longitudinal boost invariance and scaling properties in rapidity space. The influence from resonance decays on the width of the balance function is more significant but it does not destroy its boost invariance and scaling properties in rapidity space either. The balance functions in azimuthal direction are also presented.Comment: 9 figure

Characterisation Platform For Surface Metrology

The measurement and characterisation of surface texture are the most critical factors and important functionality indicators.The real surfaces is continuous, but a discrete data set is acquired by any metrology instrumentation. After a series of processes of the finite digital sample, the parameters, which are the link betweenthe surface texture, the functionality excepted and the manufacturing process, will be calculated for the surface characterisation. This platform is designed to realize the analysis and processes for the surface characterisation

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.Comment: AAAI 2016 conferenc