In this paper, a broadband metamaterial absorber with a fractional bandwidth of 126.88% was presented. The characteristic mode theory was used to guide the design of the absorber. According to the analysis of characteristic mode and characteristic current, the resistance value of resistive films can be determined. The different modal information obtained through parameter changes can also better guide the design of the absorber. To study its operation mechanism, the equivalent impedance and surface current distribution of the proposed absorber have been analyzed. The final simulation and measurement results show that the proposed absorber has a wide absorbing bandwidth which is from 3.21 to 14.35 GHz, and the absorptivity is greater than 90%, covering the S, C, X, and Ku bands. In addition, for TE and TM polarization, it can achieve an absorptivity of more than 85% at 45° oblique incident and has good angular stability. Hence, the absorber has great potential applications in the field of electromagnetic stealth technology and Radar Cross Section reduction.
Scientific workflow is a valuable tool for various complicated large-scale data processing applications. In recent years, the increasingly growing number of scientific processes available necessitates the development of recommendation techniques to provide automatic support for modelling scientific workflows. In this paper, with the help of heterogeneous information network (HIN) and tags of scientific workflows, we organize scientific workflows as a HIN and propose a novel scientific workflow similarity computation method based on metapath. In addition, the density peak clustering (DPC) algorithm is introduced into the recommendation process and a scientific workflow recommendation approach named HDSWR is proposed. The effectiveness and efficiency of our approach are evaluated by extensive experiments with real-world scientific workflows.
Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them.
To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation.
Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results.
We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.
The scattering enhancement technique has shown prominent potential in various regimes such as satellite communication, Radar Cross Section (RCS) camouflage, and remote sensing. Currently, the scattering enhancement devices based on the metasurface have shown advantages in light weight and better performance. These metasurfaces always possess complex structure, it is hard to achieve through the tradition trial-and-error method which relies on the full-wave numerical simulation. In this paper, a new method combining the machine learning and the evolution optimization algorithm is proposed to design the metasurface retroreflector (MRF) for arbitrary direction incident wave. In this method, a predicting model and a generative inverse design model are constructed and trained, the predicting model is used to evaluate the fitness of each offspring in the genetic algorithm (GA), the generative model is used to initialize the first offspring of the GA by inverse generate the MRF based on the requirements of the designer. With the assistance of these two machine learning models, the evolution optimization algorithm is employed to find the optimal design of the MRF. This approach enables automatic solution of electromagnetic inverse design problems and opens the way to facilitate the optimization of other metadevices.
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