Elastic parton scattering and non-statistical event-by-event mean-pt fluctuations in Au + Au collisions at RHIC

Non-statistical event-by-event mean-pt fluctuations in Au + Au collisions at sqrt(s_NN) = 130 and 200 GeV are analyzed in AMPT with string-melting, and the results are compared with STAR data. The analysis suggests that in-medium elastic parton scattering may contribute greatly to the mean-p_t fluctuations in relativistic heavy-ion collisions. Furthermore, it is demonstrated that non-statistical event-by-event mean-pt fluctuations can be used to probe the initial partonic dynamics in these collisions. The comparison shows that with an in-medium elastic parton scattering cross section sigma_p=10 mb, AMPT with string-melting can well reproduce sqrt(s_NN) = 130 GeV data on the centrality dependence of non-statistical event-by-event mean-pt fluctuations. The comparison also shows that the fluctuation data for sqrt(s_NN) = 200 GeV Au + Au collisions can be well reproduced with sigma_p between 6 and 10 mb.Comment: 6 pages, 3 figure

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. It will apear in a future issu