Learn More
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes(More)
Nowadays, mathematical information is increasingly available in websites and repositories, such like ArXiv, Wikipedia and growing numbers of digital libraries. Mathematical formulae are highly structured and usually presented in layout presentations, such as PDF, LATEX and Presentation MathML. The differences of presentation between text and formulae(More)
In recent years, mobile Online Social Networks (mOSNs) have gained more and more popularity. Compared with traditional social networks, mOSNs allow user's location to come into play. Thus, location sharing becomes a fundamental component of mOSNs, and many opportunities for location-based services arise which have a high value for the users. At the same(More)
Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely(More)
In consideration of feasibility, searchable encryption schemes in multi-user setting have to handle the problem of dynamical user injection and revocation, especially to make sure that user revocation will not cause security issues, such as secret key leakage. Recently, fine-grained access control using trusted third party is proposed to resolve this issue,(More)
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers(More)
With the development of cloud computing and big data, data privacy protection has become an urgent problem to solve. Data encryption is the most effective way to protect privacy; however, it will change the data format and result in: 1. database structure and application software will be changed; 2. structured query language (SQL) operations cannot work(More)
This paper is the summarized experiences of ICST team in the NTCIR-12 MathIR main tasks (ArXiv and Wikipedia main task). Our approach is based on keyword, structure and importance of formulae in a document. A novel hybrid indexing and matching model is proposed to support exact and fuzzing matching. In this hybrid model, both keyword and structure(More)
Automatic recognition and reconstruction of buildings from aerial and space images is of great practical interest for many of applications such as cartography and photo-interpretation. Building detection is the first and very difficult step in building recognition and reconstruction. It is to find buildings and separating them from the background in the(More)