MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited. A typical example is when using CNN/Daily Mail dataset for controllable text summarization, there is no guided information on the emphasis of summary sentences. A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge. Motivated by this vision, our paper introduces a new text generation dataset, named MReD. Our new dataset consists of 7,089 meta-reviews and all its 45k meta-review sentences are manually annotated with one of the 9 carefully defined categories, including abstract, strength, decision, etc. We present experimental results on start-of-the-art summarization models, and propose methods for structure-controlled generation with both extractive and abstractive models using our annotated data. By exploring various settings and analyzing the model behavior with respect to the control signal, we demonstrate the challenges of our proposed task and the values of our dataset MReD. Meanwhile, MReD also allows us to have a better understanding of the meta-review domain.Comment: 15 pages, 5 figures, accepted at ACL 202

Transcriptional up-regulation of relaxin-3 by Nur77 attenuates β-adrenergic agonist-induced apoptosis in cardiomyocytes.

The relaxin family peptides have been shown to exert several beneficial effects on the heart, including anti-apoptosis, anti-fibrosis, and anti-hypertrophy activity. Understanding their regulation might provide new opportunities for therapeutic interventions, but the molecular mechanism(s) coordinating relaxin expression in the heart remain largely obscured. Previous work demonstrated a role for the orphan nuclear receptor Nur77 in regulating cardiomyocyte apoptosis. We therefore investigated Nur77 in the hopes of identifying novel relaxin regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) data indicated that ectopic expression of orphan nuclear receptor Nur77 markedly increased the expression of latexin-3 (RLN3), but not relaxin-1 (RLN1), in neonatal rat ventricular cardiomyocytes (NRVMs). Furthermore, we found that the -adrenergic agonist isoproterenol (ISO) markedly stimulated RLN3 expression, and this stimulation was significantly attenuated in Nur77 knockdown cardiomyocytes and Nur77 knockout hearts. We showed that Nur77 significantly increased RLN3 promoter activity via specific binding to the RLN3 promoter, as demonstrated by electrophoretic mobility shift assay (EMSA) and chromatin immuno-precipitation (ChIP) assays. Furthermore, we found that Nur77 overexpression potently inhibited ISO-induced cardiomyocyte apoptosis, whereas this protective effect was significantly attenuated in RLN3 knockdown cardiomyocytes, suggesting that Nur77-induced RLN3 expression is an important mediator for the suppression of cardiomyocyte apoptosis. These findings show that Nur77 regulates RLN3 expression, therefore suppressing apoptosis in the heart, and suggest that activation of Nur77 may represent a useful therapeutic strategy for inhibition of cardiac fibrosis and heart failure. © 2018 You et al

Edge-Cloud Collaboration for Industrial IoT: An Online Approach

In this chapter, we take the Industrial Internet of Things (IIoT) as the background for studying the energy-saving resource management framework to control the cloud center (CC), edge server (ES), and terminal equipment in a closed loop. In this framework, industrial sensors collect data and transmit it to the ES for aggregation. These data form computing tasks for data analysis. Our goal is to minimize the energy consumption of the whole system while ensuring satisfied data processing accuracy and service delay of all IIoT tasks. We formulate the ES preprocessing mode selection, sensor sampling rate adaptation, and edge cloud computing and communication resource allocation as a joint optimization problem. Due to the random arrival of data and time-varying channel conditions, we introduce an online dynamic algorithm with low complexity, which efficiently solves the problem

Optimization of Extraction Process of Polysaccharide from Black Corn Kernel by Response Surface Method and Analysis of Its Antioxidant Activity

In order to explore the optimum extraction process of polysaccharide and antioxidant activity in vitro in black corn kernel. In this study, black corn kernel was used as raw material, ultrasonic-assisted extraction was applied to extract polysaccharides from black corn kernel. To explore the effects of ultrasonic power, solid-liquid ratio, extraction time, temperature and frequency on the yield of polysaccharide. The extraction process of polysaccharide from black corn kernel was optimized by response surface methodology. Besides, the antioxidant activity of the polysaccharide was investigated by measuring its scavenging ability on DPPH·, ABTS+·, and ·OH. The results showed that the extraction yield of polysaccharide from black corn kernel could reach up to 41.09%±0.59%, in these conditions: The solid-liquid ratio was 1:20 g/mL, the extraction temperature was 74 ℃, the extraction time was 60 min and the extraction frequency was 3 times. The IC50 values of scavenging rates on DPPH·, ABTS+· and ·OH were 1.959, 1.529 and 0.3554 mg/mL, respectively. Moreover, the scavenging rates showed a certain dose-effect relationship with the sample concentration, indicating that the polysaccharide had a strong antioxidant activity, thus providing a theoretical basis for further research and utilization

Traffic experiment reveals the nature of car-following

As a typical self-driven many-particle system far from equilibrium, traffic flow exhibits diverse fascinating non-equilibrium phenomena, most of which are closely related to traffic flow stability and specifically the growth/dissipation pattern of disturbances. However, the traffic theories have been controversial due to a lack of precise traffic data. We have studied traffic flow from a new perspective by carrying out large-scale car-following experiment on an open road section, which overcomes the intrinsic deficiency of empirical observations. The experiment has shown clearly the nature of car-following, which runs against the traditional traffic flow theory. Simulations show that by removing the fundamental notion in the traditional car-following models and allowing the traffic state to span a two-dimensional region in velocity-spacing plane, the growth pattern of disturbances has changed qualitatively and becomes qualitatively or even quantitatively in consistent with that observed in the experiment.Comment: 24 pages, 7 figure

Preparation, characterization, and cytotoxicity of CPT/Fe2O3-embedded PLGA ultrafine composite fibers: a synergistic approach to develop promising anticancer material

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