12,548 research outputs found
Constraints on anomalous quartic gauge couplings via production at the LHC
The vector boson scattering at the Large Hadron Collider (LHC) is sensitive
to anomalous quartic gauge couplings (aQGCs). In this paper, we investigate the
aQGC contribution to production at the LHC with
TeV in the context of an effective field theory (EFT). The unitarity bound is
applied as a cut on the energy scale of this production process, which is found
to have significant suppressive effects on the signals. To enhance the
statistical significance, we analyse the kinematic and polarization features of
the aQGC signals in detail. We find that the polarization effects induced by
the aQGCs are unique and can discriminate the signals from the SM backgrounds
well. With the proposed event selection strategy, we obtain the constraints on
the coefficients of dimension-8 operators with current luminosity. The results
indicate that the process is powerful for searching for
the and operators.Comment: 29 pages, 11 figures, 7 tables, to be published in Chinese Physics
Wave-breaking criterion and global solution for a generalized periodic coupled Camassa-Holm system
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity
Network-oriented research has been increasingly popular in many scientific
areas. In neuroscience research, imaging-based network connectivity measures
have become the key for understanding brain organizations, potentially serving
as individual neural fingerprints. There are major challenges in analyzing
connectivity matrices including the high dimensionality of brain networks,
unknown latent sources underlying the observed connectivity, and the large
number of brain connections leading to spurious findings. In this paper, we
propose a novel blind source separation method with low-rank structure and
uniform sparsity (LOCUS) as a fully data-driven decomposition method for
network measures. Compared with the existing method that vectorizes
connectivity matrices ignoring brain network topology, LOCUS achieves more
efficient and accurate source separation for connectivity matrices using
low-rank structure. We propose a novel angle-based uniform sparsity
regularization that demonstrates better performance than the existing sparsity
controls for low-rank tensor methods. We propose a highly efficient iterative
Node-Rotation algorithm that exploits the block multi-convexity of the
objective function to solve the non-convex optimization problem for learning
LOCUS. We illustrate the advantage of LOCUS through extensive simulation
studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort
neuroimaging study reveals biologically insightful connectivity traits which
are not found using the existing method
SEARS: Space Efficient And Reliable Storage System in the Cloud
Today's cloud storage services must offer storage reliability and fast data
retrieval for large amount of data without sacrificing storage cost. We present
SEARS, a cloud-based storage system which integrates erasure coding and data
deduplication to support efficient and reliable data storage with fast user
response time. With proper association of data to storage server clusters,
SEARS provides flexible mixing of different configurations, suitable for
real-time and archival applications.
Our prototype implementation of SEARS over Amazon EC2 shows that it
outperforms existing storage systems in storage efficiency and file retrieval
time. For 3 MB files, SEARS delivers retrieval time of s compared to
s with existing systems.Comment: 4 pages, IEEE LCN 201
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