13,323 research outputs found
Beam losses due to the foil scattering for CSNS/RCS
For the Rapid Cycling Synchrotron of China Spallation Neutron Source
(CSNS/RCS), the stripping foil scattering generates the beam halo and gives
rise to additional beam losses during the injection process. The interaction
between the proton beam and the stripping foil was discussed and the foil
scattering was studied. A simple model and the realistic situation of the foil
scattering were considered. By using the codes ORBIT and FLUKA, the multi-turn
phase space painting injection process with the stripping foil scattering for
CSNS/RCS was simulated and the beam losses due to the foil scattering were
obtained.Comment: Submitted to HB2012, IHEP, Beijing, Sep. 17-21, 201
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks
Anomaly detection aims to detect abnormal events by a model of normality. It
plays an important role in many domains such as network intrusion detection,
criminal activity identity and so on. With the rapidly growing size of
accessible training data and high computation capacities, deep learning based
anomaly detection has become more and more popular. In this paper, a new
domain-based anomaly detection method based on generative adversarial networks
(GAN) is proposed. Minimum likelihood regularization is proposed to make the
generator produce more anomalies and prevent it from converging to normal data
distribution. Proper ensemble of anomaly scores is shown to improve the
stability of discriminator effectively. The proposed method has achieved
significant improvement than other anomaly detection methods on Cifar10 and UCI
datasets
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
Network embedding aims to learn low-dimensional representations of nodes in a
network, while the network structure and inherent properties are preserved. It
has attracted tremendous attention recently due to significant progress in
downstream network learning tasks, such as node classification, link
prediction, and visualization. However, most existing network embedding methods
suffer from the expensive computations due to the large volume of networks. In
this paper, we propose a faster network embedding
method, called Progle, by elegantly utilizing the sparsity property of online
networks and spectral analysis. In Progle, we first construct a \textit{sparse}
proximity matrix and train the network embedding efficiently via sparse matrix
decomposition. Then we introduce a network propagation pattern via spectral
analysis to incorporate local and global structure information into the
embedding. Besides, this model can be generalized to integrate network
information into other insufficiently trained embeddings at speed. Benefiting
from sparse spectral network embedding, our experiment on four different
datasets shows that Progle outperforms or is comparable to state-of-the-art
unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and
HOPE, regarding accuracy, while is faster than the fastest
word2vec-based method. Finally, we validate the scalability of Progle both in
real large-scale networks and multiple scales of synthetic networks
Accelerated Schemes for the Minimization
In this paper, we consider the minimization for sparse recovery
and study its relationship with the - model. Based on this
relationship, we propose three numerical algorithms to minimize this ratio
model, two of which work as adaptive schemes and greatly reduce the computation
time. Focusing on two adaptive schemes, we discuss their connection to existing
approaches and analyze their convergence. The experimental results demonstrate
the proposed approaches are comparable to the state-of-the-art methods in
sparse recovery and work particularly well when the ground-truth signal has a
high dynamic range. Lastly, we reveal some empirical evidence on the exact
recovery under various combinations of sparsity, coherence, and dynamic
ranges, which calls for theoretical justification in the future.Comment: 10 page
Sharp Inequalities between Harmonic, Seiffert, Quadratic and Contraharmonic Means
In this paper, we present the greatest values , and ,
and the least values , and such that the double inequalities
, and with
, where , ,
, and
are the harmonic, Seiffert, quadratic, first
contraharmonic and second contraharmonic means of and , respectively.Comment: 11 page
Unextendible maximally entangled bases in dxd
We investigate the unextendible maximally entangled bases in
and present a -number UMEB
construction in . For higher
dimensional case, we show that for a given -number UMEB in
, there is a -number,
, UMEB in
for any . As an
example, for systems, we show that
there are at least two sets of UMEBs which are not equivalent.Comment: Errors correcte
On small set of one-way LOCC indistinguishability of maximally entangled states
In this paper, we study the one-way local operations and classical
communication (LOCC) problem. In with
, we construct a set of one-way LOCC
indistinguishable maximally entangled states which are generalized Bell states.
Moreover, we show that there are four maximally entangled states which cannot
be perfectly distinguished by one-way LOCC measurements for any dimension
.Comment: 10 pages.Very recently, we became aware of related work \cite{Zhang2}
in which the same states in
is proved to be one-way LOCC
indistinguishable. arXiv admin note: text overlap with arXiv:1310.4220 by
other autho
A Combinatorial Method for Computing Characteristic Polynomials of Starlike Hypergraphs
By using the Poisson formula for resultants and the variants of chip-firing
game on graphs, we provide a combinatorial method for computing a class of of
resultants, i.e. the characteristic polynomials of the adjacency tensors of
starlike hypergraphs including hyperpaths and hyperstars,which are given
recursively and explicitly
Hessian informed mirror descent
Inspired by the recent paper (L. Ying, Mirror descent algorithms for
minimizing interacting free energy, Journal of Scientific Computing, 84 (2020),
pp. 1-14),we explore the relationship between the mirror descent and the
variable metric method. When the metric in the mirror decent is induced by a
convex function, whose Hessian is close to the Hessian of the objective
function, this method enjoys both robustness from the mirror descent and
superlinear convergence for Newton type methods. When applied to a linearly
constrained minimization problem, we prove the global and local convergence,
both in the continuous and discrete settings. As applications, we compute the
Wasserstein gradient flows and Cahn-Hillard equation with degenerate mobility.
When formulating these problems using a minimizing movement scheme with respect
to a variable metric, our mirror descent algorithm offers a fast convergent
speed for the underlining optimization problem while maintaining the total mass
and bounds of the solution
Study on the injection optimization and transverse coupling for CSNS/RCS
The injection system of the China Spallation Neutron Source uses H- stripping
and phase space painting method to fill large ring acceptance with the linac
beam of small emittance. The emittance evolution, beam losses, and collimation
efficiency during the injection procedures for different injection parameters,
such as the injection emittances, starting injection time, twiss parameters and
momentum spread, were studied, and then the optimized injection parameters was
obtained. In addition, the phase space painting scheme which also affect the
emittance evolution and beam losses were simulated and the optimization range
of phase space painting were obtained. There will be wobble in the power supply
of the injection bumps, and the wobble effects were presented. In order to
study the transverse coupling, the injection procedures for different betatron
tunes and momentum spreads were studied.Comment: Submitted to proceedings of IPAC2012, New Orleans, Louisiana, USA,
May 20-25, 201
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