Sharp estimate of lower bound for the first eigenvalue in the Laplacian operator on compact Riemannian manifolds

The aim of this paper is give a simple proof of some results in \cite{Jun Ling-2006-IJM} and \cite{JunLing-2007-AGAG}, which are very deep studies in the sharp lower bound of the first eigenvalue in the Laplacian operator on compact Riemannian manifolds with nonnegative Ricci curvature. We also get a result about lower bound of the first Neumann eigenvalue in a special case. Indeed, our estimate of lower bound in the this case is optimal. Although the methods used in here due to \cite{Jun Ling-2006-IJM} (or \cite{JunLing-2007-AGAG}) on the whole, to some extent we can tackle the singularity of test functions and also simplify greatly much calculation in these references. Maybe this provides another way to estimate eigenvalues.Comment: 17 pages, 29 conference

Benchmarking the Privacy-Preserving People Search

People search is an important topic in information retrieval. Many previous studies on this topic employed social networks to boost search performance by incorporating either local network features (e.g. the common connections between the querying user and candidates in social networks), or global network features (e.g. the PageRank), or both. However, the available social network information can be restricted because of the privacy settings of involved users, which in turn would affect the performance of people search. Therefore, in this paper, we focus on the privacy issues in people search. We propose simulating different privacy settings with a public social network due to the unavailability of privacy-concerned networks. Our study examines the influences of privacy concerns on the local and global network features, and their impacts on the performance of people search. Our results show that: 1) the privacy concerns of different people in the networks have different influences. People with higher association (i.e. higher degree in a network) have much greater impacts on the performance of people search; 2) local network features are more sensitive to the privacy concerns, especially when such concerns come from high association peoples in the network who are also related to the querying user. As the first study on this topic, we hope to generate further discussions on these issues.Comment: 4 pages, 5 figure

Production of the top-pions at the THERA collider based γp\gamma p collisions

In the framework of the topcolor-assisted technicolor (TC2) models, we study the production of the top-pions $\pi^{0}_{t}$, $\pi_{t}^{\pm}$ via the processes $ep\to\gamma c\to\pi^{0}_{t}c$ and $ep\to\gamma c\to\pi^{\pm}_{t}b$ mediated by the anomalous top coupling $tc\gamma$. We find that the production cross section of the process $ep\to\gamma c\to\pi^{0}_{t}c$ is very small. With reasonable values of the parameters in TC2 models, the production cross section of the process $ep\to\gamma c\to\pi^{\pm}_{t}b$ can reach $1.2pb$. The charged top-pions $\pi^{\pm}_{t}$ might be directly observed via this process at the THERA collider based $\gamma p$ collisions.Comment: 10 pages, 3 figure

Coauthor prediction for junior researchers

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach. © 2013 Springer-Verlag