6,956 research outputs found
X(1576) and the Final State Interaction Effect
We study whether the broad peak X(1576) observed by BES Collaboration arises
from the final state interaction effect of decays. The
interference effect could produce an enhancement around 1540 MeV in the
spectrum with typical interference phases. However, the branching
ratio from the final state interaction effect is far less than the
experimental data.Comment: 6 pages, 4 figures. Some typos corrected, more discussion and
references adde
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1
Agent-based model with asymmetric trading and herding for complex financial systems
Background: For complex financial systems, the negative and positive
return-volatility correlations, i.e., the so-called leverage and anti-leverage
effects, are particularly important for the understanding of the price
dynamics. However, the microscopic origination of the leverage and
anti-leverage effects is still not understood, and how to produce these effects
in agent-based modeling remains open. On the other hand, in constructing
microscopic models, it is a promising conception to determine model parameters
from empirical data rather than from statistical fitting of the results.
Methods: To study the microscopic origination of the return-volatility
correlation in financial systems, we take into account the individual and
collective behaviors of investors in real markets, and construct an agent-based
model. The agents are linked with each other and trade in groups, and
particularly, two novel microscopic mechanisms, i.e., investors' asymmetric
trading and herding in bull and bear markets, are introduced. Further, we
propose effective methods to determine the key parameters in our model from
historical market data.
Results: With the model parameters determined for six representative
stock-market indices in the world respectively, we obtain the corresponding
leverage or anti-leverage effect from the simulation, and the effect is in
agreement with the empirical one on amplitude and duration. At the same time,
our model produces other features of the real markets, such as the fat-tail
distribution of returns and the long-term correlation of volatilities.
Conclusions: We reveal that for the leverage and anti-leverage effects, both
the investors' asymmetric trading and herding are essential generation
mechanisms. These two microscopic mechanisms and the methods for the
determination of the key parameters can be applied to other complex systems
with similar asymmetries.Comment: 17 pages, 6 figure
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