Efficient parallel algorithms for several problems on proper circular arc graphs are presented in this paper. These problems include finding a maximum matching, partitioning into a minimum number of induced subgraphs each of which has a Hamiltonian cycle (path), partitioning into induced subgraphs each of which has a Hamiltonian cycle (path) with at least k vertices for a given k, and adding a minimum number of edges to make the graph contain a Hamiltonian cycle (path). It is shown here that the above problems can all be solved in logarithmic time with a linear number of EREW PRAM processors, or in constant time with a linear number of BSR processors. A more important part of this work is perhaps the extension of basic BSR to allow simultaneous multiple BROADCAST instructions.

This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches.

On the basis of the relation between the multichannel prediction model and the 2-D prediction model, the convergence properties of prediction error covariance, prediction coefficients and corresponding 2-D polynomials of the latter model are derived from the known convergence properties of those of the former model. Furthermore, the formulas of the convergence order of the 2-D prediction model are obtained by using the knowledge of the convergence order of 2-D correlation sequence {γi,j}.