Unbounded violation of tripartite Bell inequalities

We prove that there are tripartite quantum states (constructed from random unitaries) that can lead to arbitrarily large violations of Bell inequalities for dichotomic observables. As a consequence these states can withstand an arbitrary amount of white noise before they admit a description within a local hidden variable model. This is in sharp contrast with the bipartite case, where all violations are bounded by Grothendieck's constant. We will discuss the possibility of determining the Hilbert space dimension from the obtained violation and comment on implications for communication complexity theory. Moreover, we show that the violation obtained from generalized GHZ states is always bounded so that, in contrast to many other contexts, GHZ states do in this case not lead to extremal quantum correlations. The results are based on tools from the theories of operator spaces and tensor norms which we exploit to prove the existence of bounded but not completely bounded trilinear forms from commutative C*-algebras.Comment: Substantial changes in the presentation to make the paper more accessible for a non-specialized reade

Assessing Software Quality through Web Comment Search and Analysis

When reusing software resources appearing on the Internet, developers often encounter the problem that it is hard to know the quality of candidate software. In this case, developers usually want to search and find referable user comment on the Internet. To assist this process, we proposed a textual comment based software quality assessment approach in this paper. It could search and collect the user comments of the software resource on the Internet automatically. Furthermore, the sentiment polarity (positive or negative) of a comment is identified and all the comments are classified into positive or negative collection. Then the quality aspects which the comment talks about are extracted so as to draw out the merits and drawbacks of software resources. With these information, developers can do candidate software selection easier and quicker in the software repository. To evaluate our approach, we apply our approach on a group of open source software. The results show that our approach could achieve satisfying precision in software quality assessment. ? 2013 Springer-Verlag Berlin Heidelberg.EI

From Topic Models to Semi-Supervised Learning: Biasing Mixed-membership Models to Exploit Topic-Indicative Features in Entity Clustering

Abstract. We present methods to introduce different forms of supervision into mixed-membership latent variable models. Firstly, we introduce a technique to bias the models to exploit topic-indicative features, i.e. features which are apriori known to be good indicators of the latent topics that generated them. Next, we present methods to modify the Gibbs sampler used for approximate inference in such models to permit injection of stronger forms of supervision in the form of labels for features and documents, along with a description of the corresponding change in the underlying generative process. This ability allows us to span the range from unsupervised topic models to semi-supervised learning in the same mixed membership model. Experimental results from an entity-clustering task demonstrate that the biasing technique and the introduction of feature and document labels provide a significant increase in clustering performance over baseline mixed-membership methods.