using-visual-world-paradigm-to-study-sentence-comprehension-mandarin

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

Tsinghua University

This special issue of the  highlights the field of advanced bioelectronics and bioinformatics. Several papers were considered for this special issue, including those on bioelectronics in wearable and implantable medical devices, such as sensors, and bioinformatics in healthcare, brain cognition, and various neural pathologies. Many investigators contributed original research articles to this issue, demonstrating emerging research fields. More than 20 papers were accepted for publication after a high-quality critical review was conducted, and 14 papers were selected for this special issue. This special issue on bioelectronics and bioinformatics attracted a substantial number of full-paper submissions from many countries. We appreciate the numerous volunteers who helped review the manuscripts. This paper provides a brief review of issues regarding bioelectronics and bioinformatics devices.

Integration of Bioelectronics and Bioinformatics: Future Direction of Bioengineering Research.

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to  to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to  to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

Article	Year
Integration of Bioelectronics and Bioinformatics: Future Direction of Bioengineering Research. Journal of medical and biological engineering\| PubMed ID: 28111531	2016
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\| PubMed ID: 28952942	2018

Huimin Ma

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