Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences.However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction.To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations.Specifically, we first generate a table feature for each relation.Then two kinds of global associations are mined from the generated table features.Next, the mined global associations are integrated into the table feature of each relation.This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step.Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table.We evaluate the proposed model on three benchmark datasets.Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets.The source code of our work is available at: https://github.com/neukg/GRTE.

The multiple-unicast conjecture in network coding states that for multiple unicast sessions in an undirected network, network coding has no advantage over routing in improving the throughput or saving bandwidth. In this paper, we propose a reduction method to study the multiple-unicast conjecture, and prove the conjecture for a new class of networks that are characterized by relations between cut-sets and source-receiver paths. This class subsumes all the known types of networks with non-zero max-flow min-cut gaps but zero coding advantage. Combining this result with a computer-aided search, we derive as a corollary that network coding is unnecessary in networks with up to six coding nodes. We also prove the multiple-unicast conjecture for almost all unit-link-length networks with up to three sessions and seven nodes.

Audio identification via fingerprint has been an active research field with wide applications for years. Many technical papers were published and commercial software systems were also employed. However, most of these previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to store on personal computers and transmit on the Internet. It would be interesting if a compressed unknown audio fragment is able to be directly recognized from the database without the fussy and time-consuming decompression-identification-recompression procedure. So far, very few algorithms run directly in the compressed domain for music information retrieval, and most of them take advantage of MDCT coefficients or derived energy type of features. As a first attempt, we propose in this paper utilizing compressed-domain spectral entropy as the audio feature to implement a novel audio fingerprinting algorithm. The compressed songs stored in a music database and the possibly distorted compressed query excerpts are first partially decompressed to obtain the MDCT coefficients as the intermediate result. Then by grouping granules into longer blocks, remapping the MDCT coefficients into 192 new frequency lines to unify the frequency distribution of long and short windows, and defining 9 new subbands which cover the main frequency bandwidth of popular songs in accordance with the scale-factor bands of short windows, we calculate the spectral entropy of all consecutive blocks and come to the final fingerprint sequence by means of magnitude relationship modeling. Experiments show that such fingerprints exhibit strong robustness against various audio signal distortions like recompression, noise interference, echo addition, equalization, band-pass filtering, pitch shifting, and slight time-scale modification etc. For 5s-long query examples which might be severely degraded, an average top-five retrieval precision rate of more than 90% can be obtained in our test data set composed of 1822 popular songs.

In this paper, we devise a novel robust music identification algorithm utilizing compressed-domain audio Zernike moment adapted from image processing techniques as the pivotal feature. Audio fingerprint derived from this feature exhibits strong robustness against various audio signal distortions including the challenging pitch shifting and time-scale modification. Experiments show that in our test dataset composed of 1822 popular songs, a 5s music query example which might have been severely corrupted is still sufficient to identify its original near-duplicate copy, with more than 90% top five precision rate.