Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

High-fat diet induces protein kinase A and G-protein receptor kinase phosphorylation of β2 -adrenergic receptor and impairs cardiac adrenergic reserve in animal hearts.

Key pointsPatients with diabetes show a blunted cardiac inotropic response to β-adrenergic stimulation despite normal cardiac contractile reserve. Acute insulin stimulation impairs β-adrenergically induced contractile function in isolated cardiomyocytes and Langendorff-perfused hearts. In this study, we aimed to examine the potential effects of hyperinsulinaemia associated with high-fat diet (HFD) feeding on the cardiac β2 -adrenergic receptor signalling and the impacts on cardiac contractile function. We showed that 8 weeks of HFD feeding leads to reductions in cardiac functional reserve in response to β-adrenergic stimulation without significant alteration of cardiac structure and function, which is associated with significant changes in β2 -adrenergic receptor phosphorylation at protein kinase A and G-protein receptor kinase sites in the myocardium. The results suggest that clinical intervention might be applied to subjects in early diabetes without cardiac symptoms to prevent further cardiac complications.AbstractPatients with diabetes display reduced exercise capability and impaired cardiac contractile reserve in response to adrenergic stimulation. We have recently uncovered an insulin receptor and adrenergic receptor signal network in the heart. The aim of this study was to understand the impacts of high-fat diet (HFD) on the insulin-adrenergic receptor signal network in hearts. After 8 weeks of HFD feeding, mice exhibited diabetes, with elevated insulin and glucose concentrations associated with body weight gain. Mice fed an HFD had normal cardiac structure and function. However, the HFD-fed mice displayed a significant elevation of phosphorylation of the β2 -adrenergic receptor (β2 AR) at both the protein kinase A site serine 261/262 and the G-protein-coupled receptor kinase site serine 355/356 and impaired adrenergic reserve when compared with mice fed on normal chow. Isolated myocytes from HFD-fed mice also displayed a reduced contractile response to adrenergic stimulation when compared with those of control mice fed normal chow. Genetic deletion of the β2 AR led to a normalized adrenergic response and preserved cardiac contractile reserve in HFD-fed mice. Together, these data indicate that HFD promotes phosphorylation of the β2 AR, contributing to impairment of cardiac contractile reserve before cardiac structural and functional remodelling, suggesting that early intervention in the insulin-adrenergic signalling network might be effective in prevention of cardiac complications in diabetes

RNA-destabilizing Factor Tristetraprolin Negatively Regulates NF-kappa B Signaling

Tristetraprolin (TTP) is a CCCH zinc finger-containing protein that destabilizes mRNA by binding to an AU-rich element. Mice deficient in TTP develop a severe inflammatory syndrome mainly because of overproduction of tumor necrosis factor alpha. We report here that TTP also negatively regulates NF-kappa B signaling at the transcriptional corepressor level, by which it may repress inflammatory gene transcription. TTP expression inhibited NF-kappa B-dependent transcription. However, overexpression of TTP did not affect reporter mRNA stability. Instead, TTP functioned as a corepressor of p65/NF-kappa B. In support of this concept, we found that TTP physically interacted with the p65 subunit of NF-kappa B and was also associated with HDAC1, -3, and -7 in vivo. Treatment with histone deacetylase inhibitors or small interfering RNA induced HDAC1 or HDAC3 knockdown completely or partly abolished the inhibitory activity of TTP on NF-kappa B reporter activation. Consistently, chromatin immuno-precipitation showed decreased recruitment of HDAC1 and increased recruitment of CREB-binding protein on the Mcp-1 promoter in TTP(-/-) cells compared with wild-type cells. Moreover, overexpression of TTP blocked CREB-binding protein-induced acetylation of p65/NF-kappa B. Taken together, these data suggest that TTP may also function in vivo as a modulator in suppressing the transcriptional activity of NF-kappa B

Tell Me What You Want: Exploring the Impact of Offering Option Repertoires on Service Performance in Gig Economy

Confronted with an increasingly competitive business landscape for credence goods in the gig economy, sellers in e-marketplaces must effectively design their services by configuring the service offering specification options to enhance the visibility of their service offerings. Motivated by the gap between the configuration of service offering specification options and its impact on service quality and sales, this study builds on the competitive repertoire theory to advance a research model that seeks to unveil how the volume, complexity, and heterogeneity of service offering specification option repertoires affect service quality and sales. We empirically examined our hypotheses with a dataset comprising 3,307 lifestyle-themed credence goods observations from Fiverr, one of the largest e-marketplaces for gig economy in the world. We discover that the repertoire volume increases both service quality and sales whereas repertoire complexity only increases service quality. Repertoire heterogeneity does not significantly impact on service quality and sales

Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images

For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://github.com/MVME-HBUT/Faster-OreFSDet.Comment: 18 pages, 11 figure

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data