Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively

Effect of Shensong Yangxin on heart failure in preserved ejection fraction rat model

Purpose: To investigate the interventional effect of ShensongYangxin (SSYX) on heart failure (HF) using preserved ejection fraction (HFpEF) rat model.Methods: HFpEF rat model was established using abdominal aorta coarctation method and randomly divided into a positive drug control group; SSYX at high, medium, and low dosage groups; and normal control group. After 8 weeks oral treatment of SSYX, echocardiography and cardiac catheterization were used to investigate the effects of SSYX on HFpEF rat cardiac functions, including mean heart rate (HR), left ventricle anterior and posterior wall thicknesses at end diastole (LVAWd + LVPWd), left ventricular internal diameter at end diastole (LVIDd), and left ventricle mass (LVM).Results: SSYX markedly decreased heart weight and improved survival rate (p < 0.01) after 12 weeks of treatment. The expression of NT-proBNP decreased in a dose-dependent manner and was significantly lower in SSYX treatment groups (p < 0.01). Compared with normal control group, expression of CaMK II, PKA and RyR2 was significantly lower (p < 0.005), while expression level of SERCA2a significantly increased after 4 g/kg/day SSYX treatment (p < 0.01).Conclusion: SSYX significantly attenuates HFpEF-induced cardiac dysfunction and increases survival rate, suggesting that SSYX may prevent HF via regulation of cytoplasmic Ca2+ handling. SSYX reduces plasma NT-proBNP levels, lending support for its therapeutic potential in HF management.Keywords: Heart failure, Ejection fraction, ShensongYangxi