Discovering Relations among Named Entities by Detecting Community Structure

PACLIC 20 / Wuhan, China / 1-3 November, 200

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work, we propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon, in which the core idea is to model the dependency between different categories of entity recognition. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. The former part automatically assigns adaptive weights across each task to achieve optimal recognition accuracy in the multi-layer network. The latter module employs the attention operation to model the dependency between different entities. In this way, our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities. Extensive experiments on public datasets verify the effectiveness of our method. Besides, we also perform ablation analyses to deeply understand our methods

ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing

Sketch-and-extrude is a common and intuitive modeling process in computer aided design. This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an unsupervised end-to-end network for discovering sketch and extrude from point clouds. Behind ExtrudeNet are two new technical components: 1) an effective representation for sketch and extrude, which can model extrusion with freeform sketches and conventional cylinder and box primitives as well; and 2) a numerical method for computing the signed distance field which is used in the network learning. This is the first attempt that uses machine learning to reverse engineer the sketch-and-extrude modeling process of a shape in an unsupervised fashion. ExtrudeNet not only outputs a compact, editable and interpretable representation of the shape that can be seamlessly integrated into modern CAD software, but also aligns with the standard CAD modeling process facilitating various editing applications, which distinguishes our work from existing shape parsing research. Code is released at https://github.com/kimren227/ExtrudeNet.Comment: Accepted to ECCV 202

(Section A: Planning Strategies and Design Concepts)

This paper focus on the integration of multi-planning in the widespread small and medium-sized cities in China, which are now facing embarrassment in the process of urbanisation. As the basic executors within the three-level administrative system, small and medium-sized cities are being trapped in the multifaceted dilemma of population loss, constrained spatial and natural resources and less positive policies. In order to find an optimized approach to achieve urban transformation while responding to these practical problems, this paper proposes spatial planning that collates and integrates all of the current plans completely, eliminating their discrepancies and forming one blueprint for the city. This is a new approach leading the transformation of small and medium-sized cities. This approach must be comprehensive, multi tasking, highly exercisable and localised, and balanced between economic growth and environmental improvement in order to better the urban and rural life of these numerous small and medium-sized cities

Quantitative control of idealized analysis models of thin designs

When preparing a design model for engineering analysis, model idealization is often used, where defeaturing, and/or local dimension reduction of thin regions, are carried out. This simplifies the analysis, but quantitative estimates of the idealization error, the analysis error caused by this idealization, are necessary if the results are to be of practical use. The paper focuses on a posteriori estimation of such idealization error, via both a theoretical analysis and practical algorithms. Our approach can compute bounds for the errors induced by dimension reduction, defeaturing or both in combination. Performance of our error estimate is demonstrated using examples

How do executive excess compensation affect enterprise technological innovation: evidence from a panel threshold model of Chinese biopharmaceutical companies

This study examines the levels of executive excess compensation (EEC) that stimulate the quality and efficiency of enterprise technology innovation (ETI). Using a behavioral agency perspective, we investigate how companies achieve superior ETI by providing sufficient incentives to motivate executives to perform to the best of their abilities. We use a panel threshold model based on a sample of Chinese-listed biopharmaceutical companies and find that: (1) providing an EEC between 0.0592 and 0.1907 significantly affects the promotion of ETI quality; (2) regarding ETI efficiency, executives generally do not receive the compensation that they deserve; and (3) the existing EEC has a weak negative impact on ETI efficiency, gradually disappearing as compensation increases. Heterogeneity analysis reveals that restricting EEC to the eastern area and strengthening the supervision of EEC in state-owned enterprises are effective measures for stimulating ETI. We advance the literature by providing guidance on compensation plans to companies in different regions

Internationalization of transnational entrepreneurial firms from an advanced to emerging economy: the role of transnational mixed-embeddedness

Purpose: This study investigates the role of transnational mixed-embeddedness when transnational entrepreneurial firms (TEFs) become internationalized. First-generation immigrant entrepreneurs who maintain business arrangements in their home and host countries own TEFs. In many cases, they internationalize from emerging economies to advanced economies. Nevertheless, this study focuses on TEF cases that internationalize from an advanced to an emerging economy, which prior transnational entrepreneurship studies have largely overlooked. Design/methodology/approach: This research uses a qualitative approach based on six TEF case studies from Canada and the UK venturing into China to explore TEFs' internationalization. Findings: The case studies explore the elements that constitute TEFs' cognitive and relational embeddedness—two main types of embeddedness—in home and host countries and how TEFs exploit such embeddedness for their internationalization. The results suggest that high levels of transnational mixed-embeddedness help TEFs reduce resource and institutional distance barriers in home countries, thereby assisting their internationalization. A framework that visualizes the role of transnational mixed-embeddedness in TEFs' internationalization and novel categorizations of transnational mixed-embeddedness is proposed. Originality/value: Although there has been a growing demand for research on the emergence of internationalized smaller firms, there have been few empirical efforts on TEFs' internationalization. It is still unclear how TEFs internationalize differently than homegrown entrepreneurial firms. This study fills this gap in transnational entrepreneurship literature by examining the influence of transnational mixed-embeddedness on TEFs' internationalization

Auxin efflux controls orderly nucellar degeneration and expansion of the female gametophyte in Arabidopsis

The nucellus tissue in flowering plants provides nutrition for the development of the female gametophyte (FG) and young embryo. The nucellus degenerates as the FG develops, but the mechanism controlling the coupled process of nucellar degeneration and FG expansion remains largely unknown. The degeneration process of the nucellus and spatiotemporal auxin distribution in the developing ovule before fertilization were investigated in Arabidopsis thaliana. Nucellar degeneration before fertilization occurs through vacuolar cell death and in an ordered degeneration fashion. This sequential nucellar degeneration is controlled by the signalling molecule auxin. Auxin efflux plays the core role in precisely controlling the spatiotemporal pattern of auxin distribution in the nucellus surrounding the FG. The auxin efflux carrier PIN1 transports maternal auxin into the nucellus while PIN3/PIN4/PIN7 further delivers auxin to degenerating nucellar cells and concurrently controls FG central vacuole expansion. Notably, auxin concentration and auxin efflux are controlled by the maternal tissues, acting as a key communication from maternal to filial tissue