Zobrazeno 1 - 10
of 253
pro vyhledávání: '"Lu, Ruqian"'
Autor:
Lu, Ruqian, Zhang, Shuhan
While there have been lots of work studying frequent subgraph mining, very rare publications have discussed frequent subnet mining from more complicated data structures such as Petri nets. This paper studies frequent subnets mining from a single larg
Externí odkaz:
http://arxiv.org/abs/2103.11342
Autor:
Lu, Ruqian, Zhang, Shuhan
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets. We introduced the concept of complete subnets and the net graph representation. PSpan transforms Petri nets in net graphs and per
Externí odkaz:
http://arxiv.org/abs/2101.11972
Autor:
Lu, Ruqian, Hou, Shengluan
We propose a novel paradigm of semi-supervised learning (SSL)--the semi-supervised multiple representation behavior learning (SSMRBL). SSMRBL aims to tackle the difficulty of learning a grammar for natural language parsing where the data are natural
Externí odkaz:
http://arxiv.org/abs/1910.09292
Autor:
Hou, Shengluan, Lu, Ruqian
Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora. To make the summarization results more faithful, this paper presents an unsupervis
Externí odkaz:
http://arxiv.org/abs/1910.05915
This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal
Externí odkaz:
http://arxiv.org/abs/1909.00923
We introduce some new perfect state transfer and teleportation schemes by quantum walks with two coins. Encoding the transferred information in coin 1 state and alternatively using two coin operators, we can perfectly recover the information on coin
Externí odkaz:
http://arxiv.org/abs/1802.02400
Autor:
Li, Yangyang, Lu, Ruqian
Symmetric positive definite (SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimen
Externí odkaz:
http://arxiv.org/abs/1703.09499
Autor:
Li, Yangyang, Lu, Ruqian
Traditional manifold learning algorithms often bear an assumption that the local neighborhood of any point on embedded manifold is roughly equal to the tangent space at that point without considering the curvature. The curvature indifferent way of ma
Externí odkaz:
http://arxiv.org/abs/1703.10675
