15,412 research outputs found
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
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