43,354 research outputs found
Statistical and dynamical decoupling of the IGM from Dark Matter
The mean mass densities of cosmic dark matter is larger than that of baryonic
matter by a factor of about 5 in the CDM universe. Therefore, the
gravity on large scales should be dominant by the distribution of dark matter
in the universe. However, a series of observations incontrovertibly show that
the velocity and density fields of baryonic matter are decoupling from
underlying dark matter field. This paper shows our attemps to unveil the
physics behind this puzzle. In linear approximation, the dynamics of the baryon
fluid is completely governed by the gravity of the dark matter. Consequently,
the mass density field of baryon matter will be
proportional to that of dark matter , even though
they are different from each other initially. In weak and moderate nonlinear
regime, the dynamics of the baryon fluid can be sketched by Burgers equation. A
basic feature of the Burgers dynamics is to yield shocks. When the Reynolds
number is large, the Burgers fluid will be in the state of Burgers turbulence,
which consists of shocks and complex structures. On the other hand, the
collisionless dark matter may not show such shock, but a multivalued velocity
field. Therefore, the weak and moderate nonlinear evolution leads to the
IGM-dark matter deviation. Yet, the velocity field of Burgers fluid is still
irrotational, as gravity is curl-free. In fully nonlinear regime, the vorticity
of velocity field developed, and the cosmic baryonic fluid will no longer be
potential, as the dynamics of vorticity is independent of gravity and can be
self maintained by the nonlinearity of hydrodynamics. In this case, the cosmic
baryon fluid is in the state of fully developed turbulence, which is
statistically and dynamically decoupling from dark matter. This scenario
provides a mechanism of cohenent explanation of observations.Comment: 21 page
Complete bounded -hypersurfaces in the weighted volume-preserving mean curvature flow
In this paper, we study the complete bounded -hypersurfaces in
weighted volume-preserving mean curvature flow. Firstly, we investigate the
volume comparison theorem of complete bounded -hypersurfaces with
and get some applications of the volume comparison theorem.
Secondly, we consider the relation among , extrinsic radius ,
intrinsic diameter , and dimension of the complete
-hypersurface, and we obtain some estimates for the intrinsic diameter
and the extrinsic radius. At last, we get some topological properties of the
bounded -hypersurface with some natural and general restrictions
Transfer Learning across Networks for Collective Classification
This paper addresses the problem of transferring useful knowledge from a
source network to predict node labels in a newly formed target network. While
existing transfer learning research has primarily focused on vector-based data,
in which the instances are assumed to be independent and identically
distributed, how to effectively transfer knowledge across different information
networks has not been well studied, mainly because networks may have their
distinct node features and link relationships between nodes. In this paper, we
propose a new transfer learning algorithm that attempts to transfer common
latent structure features across the source and target networks. The proposed
algorithm discovers these latent features by constructing label propagation
matrices in the source and target networks, and mapping them into a shared
latent feature space. The latent features capture common structure patterns
shared by two networks, and serve as domain-independent features to be
transferred between networks. Together with domain-dependent node features, we
thereafter propose an iterative classification algorithm that leverages label
correlations to predict node labels in the target network. Experiments on
real-world networks demonstrate that our proposed algorithm can successfully
achieve knowledge transfer between networks to help improve the accuracy of
classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201
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