7,765 research outputs found
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[aquaÂ[μ3-4-carbÂoxy-2-(pyridin-4-yl)-1H-imidazole-5-carboxylÂato-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 octaÂhedral NiN3O3 coordÂination mode and is N,O-chelated by two deprotonated 2-(pyridin-4-yl)-1H-imidazole-4,5-dicarbÂoxyÂlic 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 supraÂmolecular 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
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