12,235 research outputs found
Lattice calculation of hadronic tensor of the nucleon
We report an attempt to calculate the deep inelastic scattering structure
functions from the hadronic tensor calculated on the lattice. We used the
Backus-Gilbert reconstruction method to address the inverse Laplace
transformation for the analytic continuation from the Euclidean to the
Minkowski space.Comment: 8 pages, 5 figures; Proceedings of the 35th International Symposium
on Lattice Field Theory, 18-24 June 2017, Granada, Spai
Variance Reduction and Cluster Decomposition
It is a common problem in lattice QCD calculation of the mass of the hadron
with an annihilation channel that the signal falls off in time while the noise
remains constant. In addition, the disconnected insertion calculation of the
three-point function and the calculation of the neutron electric dipole moment
with the term suffer from a noise problem due to the
fluctuation. We identify these problems to have the same origin and the
problem can be overcome by utilizing the cluster decomposition
principle. We demonstrate this by considering the calculations of the glueball
mass, the strangeness content in the nucleon, and the CP violation angle in the
nucleon due to the term. It is found that for lattices with physical
sizes of 4.5 - 5.5 fm, the statistical errors of these quantities can be
reduced by a factor of 3 to 4. The systematic errors can be estimated from the
Akaike information criterion. For the strangeness content, we find that the
systematic error is of the same size as that of the statistical one when the
cluster decomposition principle is utilized. This results in a 2 to 3 times
reduction in the overall error.Comment: 7 pages, 5 figures, appendix added to address the systematic erro
Latent Class Model with Application to Speaker Diarization
In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance
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