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

Some analysis on mobile-agent based network routing

©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Deployment of mobile agents in network-based applications has attracted lots of attentions in recent years. How to control the activities of agents is crucial for effective application of mobile agents. This paper focuses on the application of mobile agents in network routing. Two important activity properties of mobile agents are identified: the probability of success (the probability of finding the destination) and the distribution of mobile agents running in the network. To our knowledge, little work has been done on these two aspects. Our results show that the number of mobile agents can be controlled by adjusting the number of agents generated per request and the number of jumps each mobile agent can move. Thus, we can improve network performance by tuning relevant parameters.Wenyu Qu, Hong She

A Diachronic Study on the Cultivation of Innovative Talents in China’s Foreign Language Education: Achievements and Prospects

It is the historical mission and sacred duty of Chinese universities to cultivate innovative talents. Researchers in the domain of foreign language education have undertaken in-depth theoretical research around the cultivation of innovative talents in the past few decades. Taking 35 academic articles published in the Chinese Social Sciences Citation Index (CSSCI) as the research sample, this study uses textual analysis to conduct a diachronic review of the generation background, concept definition, quality indicators, and educational mechanisms to discover a wealth of innovative wisdom in the academic community. However, there is a lack of research in areas such as teacher education and educational evaluation, which will require focused attention in the future

Algebraic Rreconstruction for Parallel Imaging with Radial Trajectory

A novel algebraic reconstruction method for parallel imaging with radial trajectory is proposed. Taking advantage of projection-slice theorem, the reconstruction can be performed by first carrying out a 1-D FFT and then iteratively solving a system of linear equations. Since the data in non-Cartesian coordinate are directly used, the bulk work of gridding is not required. The feasibility of this method was demonstrated by simulation.published_or_final_versio