arXiv subjects

Di Wang

Publications and source records attributed to Di Wang.

616 records · Page 35Linked to original sources

arXiv2014

Dirac and Weyl Semimetal in XYBi (X=Ba, Eu; Y=Cu, Ag and Au)

Weyl and Dirac semimetals recently stimulate intense research activities due to their novel properties. Combining first-principles calculations and effective model analysis, we predict that nonmagnetic compounds BaYBi (Y=Au, Ag and Cu) are Dirac semimetals. As for the magnetic compound EuYBi, although the time reversal symmetry is broken, their long-range magnetic ordering cannot split the Dirac point into pairs of Weyl points. However, we propose that partially substitute Eu ions by Ba ions will realize the Weyl semimetal.

cond-mat.mtrl-sci↗

arXiv2014

On the Security of Trustee-based Social Authentications

Recently, authenticating users with the help of their friends (i.e., trustee-based social authentication) has been shown to be a promising backup authentication mechanism. A user in this system is associated with a few trustees that were selected from the user's friends. When the user wants to regain access to the account, the service provider sends different verification codes to the user's trustees. The user must obtain at least k (i.e., recovery threshold) verification codes from the trustees before being directed to reset his or her password. In this paper, we provide the first systematic study about the security of trustee-based social authentications. Specifically, we first introduce a novel framework of attacks, which we call forest fire attacks. In these attacks, an attacker initially obtains a small number of compromised users, and then the attacker iteratively attacks the rest of users by exploiting trustee-based social authentications. Then, we construct a probabilistic model to formalize the threats of forest fire attacks and their costs for attackers. Moreover, we introduce various defense strategies. Finally, we apply our framework to extensively evaluate various concrete attack and defense strategies using three real-world social network datasets. Our results have strong implications for the design of more secure trustee-based social authentications.

cs.CR↗

arXiv2010

An Explicit Nonlinear Mapping for Manifold Learning

Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, that there is no explicit mappings from the input data manifold to the output embedding. This prohibits the application of manifold learning methods in many practical problems such as classification and target detection. Previously, in order to provide explicit mappings for manifold learning methods, many methods have been proposed to get an approximate explicit representation mapping with the assumption that there exists a linear projection between the high-dimensional data samples and their low-dimensional embedding. However, this linearity assumption may be too restrictive. In this paper, an explicit nonlinear mapping is proposed for manifold learning, based on the assumption that there exists a polynomial mapping between the high-dimensional data samples and their low-dimensional representations. As far as we know, this is the first time that an explicit nonlinear mapping for manifold learning is given. In particular, we apply this to the method of Locally Linear Embedding (LLE) and derive an explicit nonlinear manifold learning algorithm, named Neighborhood Preserving Polynomial Embedding (NPPE). Experimental results on both synthetic and real-world data show that the proposed mapping is much more effective in preserving the local neighborhood information and the nonlinear geometry of the high-dimensional data samples than previous work.

cs.CV↗

arXiv2009

Enhancing network transmission capacity by efficiently allocating node capability

A network's transmission capacity is the maximal rate of traffic inflow that the network can handle without causing congestion. Here we study how to enhance this quantity by redistributing the capability of individual nodes while preserving the total sum of node capability. We propose a practical and effective node-capability allocation scheme which allocates a node's capability based on the local knowledge of the node's connectivity. We show the scheme enhances the transmission capacity by two orders of magnitude for networks with heterogenous structures.

cs.NI↗