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 $\Lambda$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 $\rho_b({\bf r},t)$ will be proportional to that of dark matter $\rho_{\rm dm}({\bf r},t)$, 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 λ\lambda-hypersurfaces in the weighted volume-preserving mean curvature flow

In this paper, we study the complete bounded $\lambda$-hypersurfaces in weighted volume-preserving mean curvature flow. Firstly, we investigate the volume comparison theorem of complete bounded $\lambda$-hypersurfaces with $|A|\leq\alpha$ and get some applications of the volume comparison theorem. Secondly, we consider the relation among $\lambda$, extrinsic radius $k$, intrinsic diameter $d$, and dimension $n$ of the complete $\lambda$-hypersurface, and we obtain some estimates for the intrinsic diameter and the extrinsic radius. At last, we get some topological properties of the bounded $\lambda$-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

A Study on James Legge’s English Translation of Lun Yu

Lun yu, a masterpiece of ancient Chinese philosophy, is among the most influential books in Chinese history. Legge’s English translation of lun yu has stood the test of time, distinguished as the standard work by which subsequent translations of the classics have been judged. The most remarkable quality of Legge’s version is its faithfulness in content, owing to Legge’s scholarly industry and keen perception of the original. Legge ensured a comprehension of Confucianism from his readership which is similar to the general Chinese interpretation. In spite of some referential and pragmatic inaccuracy, any careful, patient and judicious Western reader is able to perceive Confucius’ key concepts (Ren, Li, etc.), and his ethical, political, philosophical and educational thoughts through Legge’s translation. Legge had made a great contribution to the introduction of Confucianism and the Chinese culture to the West.Key words: Lun yu; Legge; translation; faithfulnes