Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset

Electrospun polyvinyl alcohol/carbon dioxide modified polyethyleneimine composite nanofiber scaffolds

A novel biocompatible polyvinyl alcohol/carbon dioxide modified polyethyleneimine (PVA/PEI-CO2) composite nanofiber was fabricated by a green and facile protocol, which reduces the cytotoxicity of PEI through the surface modification of the PEI with CO2. The 13C NMR spectrum, elemental analysis, and TGA show that CO2 has been incorporated in the PEI surface resulting in a relatively stable structure. The resulting PVA/PEI-CO2 composite nanofibers have been characterized by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), contact angle, and scanning electron microscopy (SEM). The results show that the average diameters of the nanofibers range from 265 ± 53 nm to 423 ± 80 nm. The cytotoxicity of PVA/PEI-CO2 composite nanofibers was assessed by cytotoxicity evaluation using the growth and cell proliferation of normal mice Schwann cells. SEM and the MTT assay demonstrated the promotion of cell growth and proliferation on the PVA/PEI-CO2 composite scaffold. It suggests that PEI-CO2 can have tremendous potential applications in biological material research

Topic-Guided Self-Introduction Generation for Social Media Users

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling

Poly[aqua­[μ3-4-carb­oxy-2-(pyridin-4-yl)-1H-imidazole-5-carboxyl­ato-κ5 N 1,O 5:N 3,O 4:N 2]nickel(II)]

The water-coordinated Ni2+ cation in the title compound, [Ni(C10H5N3O4)(H2O)]n, assumes an octa­hedral NiN3O3 coord­ination mode and is N,O-chelated by two deprotonated 2-(pyridin-4-yl)-1H-imidazole-4,5-dicarb­oxy­lic acid (HPyImDC2−) ligands, forming a layer structure extending in the bc plane. The chains are arranged along the b-axis direction, forming a layer structure extending in the bc plane. O—H⋯O hydrogen bonding between the layers results in the formation of a three-dimensional supra­molecular framework. The structure is isotypic with the Zn analogue [Li et al. (2009). Cryst. Growth Des. 6, 3423–3431]

Design of a compact microfludic device for controllable cell distribution

A compact microfluidic device with 96 microchambers allocated within four circular units was designed and examined for cell distribution. In each unit, cells were distributed to the surrounding chambers radially from the center. The circular arrangement of the chambers makes the design simple and compact. A controllable and quantitative cell distribution is achievable in this device. This design is significant to the microfluidic applications where controllable distribution of cells in multipule microchambers is demanded.<br /

A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.co