Detecting (Un)Important Content for Single-Document News Summarization

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the "beginning of document" heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.Comment: Accepted By EACL 201

Small RNA Deep Sequencing Reveals Role for Arabidopsis thaliana RNA-Dependent RNA Polymerases in Viral siRNA Biogenesis

RNA silencing functions as an important antiviral defense mechanism in a broad range of eukaryotes. In plants, biogenesis of several classes of endogenous small interfering RNAs (siRNAs) requires RNA-dependent RNA Polymerase (RDR) activities. Members of the RDR family proteins, including RDR1and RDR6, have also been implicated in antiviral defense, although a direct role for RDRs in viral siRNA biogenesis has yet to be demonstrated. Using a crucifer-infecting strain of Tobacco Mosaic Virus (TMV-Cg) and Arabidopsis thaliana as a model system, we analyzed the viral small RNA profile in wild-type plants as well as rdr mutants by applying small RNA deep sequencing technology. Over 100,000 TMV-Cg-specific small RNA reads, mostly of 21- (78.4%) and 22-nucleotide (12.9%) in size and originating predominately (79.9%) from the genomic sense RNA strand, were captured at an early infection stage, yielding the first high-resolution small RNA map for a plant virus. The TMV-Cg genome harbored multiple, highly reproducible small RNA-generating hot spots that corresponded to regions with no apparent local hairpin-forming capacity. Significantly, both the rdr1 and rdr6 mutants exhibited globally reduced levels of viral small RNA production as well as reduced strand bias in viral small RNA population, revealing an important role for these host RDRs in viral siRNA biogenesis. In addition, an informatics analysis showed that a large set of host genes could be potentially targeted by TMV-Cg-derived siRNAs for posttranscriptional silencing. Two of such predicted host targets, which encode a cleavage and polyadenylation specificity factor (CPSF30) and an unknown protein similar to translocon-associated protein alpha (TRAP α), respectively, yielded a positive result in cleavage validation by 5′RACE assays. Our data raised the interesting possibility for viral siRNA-mediated virus-host interactions that may contribute to viral pathogenicity and host specificity

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction

A Novel Model of Working Set Selection for SMO Decomposition Methods

In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.Comment: 8 pages, 12 figures, it was submitted to IEEE International conference of Tools on Artificial Intelligenc

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table