True Nonlinear Dynamics from Incomplete Networks

We study nonlinear dynamics on complex networks. Each vertex $i$ has a state $x_i$ which evolves according to a networked dynamics to a steady-state $x_i^*$. We develop fundamental tools to learn the true steady-state of a small part of the network, without knowing the full network. A naive approach and the current state-of-the-art is to follow the dynamics of the observed partial network to local equilibrium. This dramatically fails to extract the true steady state. We use a mean-field approach to map the dynamics of the unseen part of the network to a single node, which allows us to recover accurate estimates of steady-state on as few as 5 observed vertices in domains ranging from ecology to social networks to gene regulation. Incomplete networks are the norm in practice, and we offer new ways to think about nonlinear dynamics when only sparse information is available.Comment: AAAI 2020, 9 pages, 5 figure

Myeloperoxidase gene-463G > A polymorphism and premature coronary artery disease

We investigated the association between myeloperoxidase gene -463G > A polymorphism and premature coronary artery disease (CAD) in two Chinese population samples: 229 patients and 230 controls. Genotypes were determined by ligase detection reaction-polymerase chain reaction sequencing and the grouping technique. We found lower frequencies of both the A/A genotype and the A allele in patients (p < 0.05). Multivariate logistic regression showed that the risk of premature CAD in subjects carrying the AA genotype was reduced by 83% in relation to individuals carrying the G/G genotype (OR = 0.172, 95% CI: 0.057-0.526, p = 0.002). Our results indicate that -463G > A polymorphism of the myeloperoxidase gene is associated with premature CAD in Chinese individuals, suggesting that the AA genotype is a protective factor against premature CAD

Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary

Text information contained in scene images is very helpful for high‐level image understanding. In this study, the authors propose to learn co‐occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids to incorporate spatial information, the authors SED associates every codeword with a particular response region and introduces more precise spatial information for robust character recognition. After localised soft coding and max pooling of the first layer, a sparse dictionary is learned to model co‐occurrence of several local strokes, which further improves classification performance. Experimental results on two scene character recognition datasets ICDAR2003 and CHARS74 K demonstrate that their character recognition method outperforms state‐of‐the‐art methods. Besides, competitive word recognition results are also reported for four benchmark word recognition datasets ICDAR2003, ICDAR2011, ICDAR2013 and street view text when combining their character recognition method with a conditional random field language model

Scene text recognition using part-based tree-structured character detection

demonstrate that the proposed method outperforms stateof-the-art methods significantly both for character detection and word recognition