Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.     

Do funded papers attract more usage?

Research funding has been seen as one of the most important resource in the reward system of science. And usage of publications denotes an interesting perspective of user behavior in scientific communication. This study aims to address the relationship between funding and Usage Count, which is a new metrics item established on the platform of Web of Science. Full records of 300,010 articles published in 2013 were downloaded in October 2015, and divided into six disciplines, including information science library science, education educational research, economics, computer science, materials science, and chemistry. Seven indicators were proposed to measure the impact, including Funding rate, Citation per paper, Usage rate, Usage per paper, Citation difference, Usage difference, and Conversion rate. It concluded funding has impact on usage and citation, and funded papers attract more usage, but varying in different disciplines. Usage Count can be used in the extension of citation metrics but with limits. This study originally engages with usage metrics and detected that there is positive correlation between usage and funding.     

太赫兹超表面计算全息

全息术是一种三维成像技术,它已经被应用于多种实际场景。随着计算机科学与技术的迅猛发展,计算全息由于其方便和灵活的特性,已经成为一种广泛应用的全息成像方法。本文回顾了我们近期基于超表面的太赫兹计算全息研究进展。其中,作为全息板的超表面展示出了超越传统光学器件的独特性能。首先,利用超表面实现了对于全息板每个像素的相位振幅同时且独立的调控,进而实现了高质量全息成像。这种新的电磁波操控能力也带来了新的全息成像效果,如利用介质超表面实现了全息像沿传播方向上的连续变化。其次,对超表面在不同偏振态下的响应进行设计,分别实现了线偏振态与频率复用、圆偏振态复用、以及基于表面波的偏振复用超表面全息术。此外,本文提出了依赖于温度变化而主动可控的超表面全息术,为今后计算全息术的设计与实现提供了新的方案,也推动了超表面在实际应用方面的发展。     

A multi-criteria context-sensitive approach for social image collection summarization

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.     

Synthesis of Cellulose-Based Macromolecular Coupling Agent and its Utilization in Poly (butylene succinate)/Wood Flour Composites

A long fatty side chain was introduced into the macromolecule of hydroxyethyl cellulose (HEC) via esterification reaction. The hydrophobicity of hydroxyethyl cellulose lauric acid ester (HECLAE) was enhanced in comparison with HEC. The obtained HECLAE was used as macromolecular coupling agent in poly (butylene succinate)/wood flour composites and exhibited a positive influence on improving the mechanical performance of composites. Besides, HECLAE plays a role as a hydrophobic agent in composites. A significant increase in storage modulus (E’) was observed upon the incorporation of treated wood flour. SEM images showed that the dispersion of treated wood flour in PBS matrix was improved.