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Clean air captures attention whereas pollution distracts: evidence from brain activities

Frontiers of Environmental Science & Engineering 2023, Volume 18, Issue 4, doi: 10.1007/s11783-024-1801-x

Abstract:

● We find air pollution distracts attention and reveal the neurocognitive

Keywords： Air pollution     Attention     Disengagement     Performance     Event-related potential    

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Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1621-4

Abstract:

● Used a double-stage attention mechanism model to predict ozone.

Keywords： Ozone prediction     Deep learning     Time series     Attention     Volatile organic compounds    

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Less attention paid to waterborne SARS-CoV-2 spreading in Beijing urban communities

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 5, doi: 10.1007/s11783-021-1398-2

Abstract:

• A survey on individual’s perception of SARS-CoV-2 transmission was conducted.

Keywords： Environmental dissemination     Risk communication     Individual perception    

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Erratum to: Meter-scale variation within a single transect demands attention to taxon accumulation curves

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 6, doi: 10.1007/s11783-022-1560-5

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Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 5, doi: 10.1007/s11783-022-1543-6

Abstract:

● Riverine microbiomes exhibited hyperlocal variation within a single transect.

Keywords： Microbiome     Freshwater     Taxon accumulation curve     Community assembly    

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Endothelial dysfunction in COVID-19 calls for immediate attention: the emerging roles of the endothelium

Weijian Hang, Chen Chen, Xin A. Zhang, Dao Wen Wang

Frontiers of Medicine 2021, Volume 15, Issue 4,   Pages 638-643 doi: 10.1007/s11684-021-0831-z

Abstract: We are calling for closer attention to endothelial dysfunction in COVID-19 not only for fully revealing

Keywords： COVID-19     endothelial dysfunction     inflammation reaction     cytokine storm    

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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learning on graph-structured data. However, as the number of network layers increases, GNNs based on the neighborhood aggregation strategy deteriorate due to the problem of oversmoothing, which is the major bottleneck for applying GNNs to real-world graphs. Many efforts have been made to improve the process of feature information aggregation from directly connected nodes, i.e., breadth exploration. However, these models perform the best only in the case of three or fewer layers, and the performance drops rapidly for deep layers. To alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised manner. In addition to breadth exploration, a -layer NGAT uses a layer-wise aggregation strategy guided by the mechanism to selectively leverage feature information from the -order neighborhood, i.e., depth exploration. Even with a 10-layer or deeper architecture, NGAT can balance the need for preserving the locality (including root node features and the local structure) and aggregating the information from a large neighborhood. In a number of experiments on standard tasks, NGAT outperforms other novel models and achieves state-of-the-art performance.

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

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Efficient decoding self-attention for end-to-end speech synthesis Research Article

Wei ZHAO, Li XU

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1127-1138 doi: 10.1631/FITEE.2100501

Abstract: has been innovatively applied to text-to-speech (TTS) because of its parallel structure and superior strength in modeling sequential data. However, when used in with an autoregressive decoding scheme, its inference speed becomes relatively low due to the quadratic complexity in sequence length. This problem becomes particularly severe on devices without graphics processing units (GPUs). To alleviate the dilemma, we propose an (EDSA) module as an alternative. Combined with a dynamic programming decoding procedure, TTS model inference can be effectively accelerated to have a linear computation complexity. We conduct studies on Mandarin and English datasets and find that our proposed model with EDSA can achieve 720% and 50% higher inference speed on the central processing unit (CPU) and GPU respectively, with almost the same performance. Thus, this method may make the deployment of such models easier when there are limited GPU resources. In addition, our model may perform better than the baseline Transformer TTS on out-of-domain utterances.

Keywords： Efficient decoding     End-to-end     Self-attention     Speech synthesis    

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Attention-based encoder-decoder model for answer selection in question answering Article

Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 535-544 doi: 10.1631/FITEE.1601232

Abstract: In this paper, we introduce an attention-based deep learning model to address the answer selection taskOur model also uses a step attention mechanism which allows the question to focus on a certain part of

Keywords： Question answering     Answer selection     Attention     Deep learning    

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Filter-cluster attention based recursive network for low-light enhancement Research Article

Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN,hzxcyanwind@163.com,lijinjiang@gmail.com-

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 7,   Pages 1028-1044 doi: 10.1631/FITEE.2200344

Abstract: FCA and self-attention are used to highlight the low-light regions and important channels of the feature

Keywords： Low-light enhancement     Filter-cluster attention     Dense connection pyramid     Recursive network    

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Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-basedThree spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information

Keywords： Robot grasp detection     Attention mechanism     Encoder–     decoder     Neural network    

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EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum Research Article

Yunzhan ZHOU, Tian FENG, Shihui SHUAI, Xiangdong LI, Lingyun SUN, Henry Been-Lirn DUH,yunzhan.zhou@durham.ac.uk,t.feng@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 101-112 doi: 10.1631/FITEE.2000318

Abstract: Predicting facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience. Explorations toward development of a mechanism using eye-tracking data have so far been limited to 2D cases, and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective. We present the first 3D Eye-tracking Dataset for modeling in a virtual Museum, known as the EDVAM. In addition, a model is devised and tested with the EDVAM to predict a user's subsequent from previous eye movements. This work provides a reference for modeling and context-aware interaction in the context of .

Keywords： Visual attention     Virtual museums     Eye-tracking datasets     Gaze detection     Deep learning    

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Progress in the research and development of p-xylene liquid phase oxidation process

WANG Lijun, CHENG Youwei, WANG Qinbo, LI Xi

Frontiers of Chemical Science and Engineering 2007, Volume 1, Issue 3,   Pages 317-326 doi: 10.1007/s11705-007-0058-9

Abstract: reaction engineering should be developed to process system engineering to extend its scope, and particular attention

Keywords： development     -xylene     Zhejiang University     particular attention     viewpoint    

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Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 5,   Pages 732-744 doi: 10.1007/s11709-023-0965-y

Abstract: Finally, an efficient attention module that significantly improves the anti-interference ability of the

Keywords： tunnel engineering     crack segmentation     fast detection     DeepLabv3+     feature fusion     attention mechanism    

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Dynamic response of a long span suspension bridge and running safety of a train under wind action

GUO Weiwei, XIA He, XU You-lin

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 1,   Pages 71-79 doi: 10.1007/s11709-007-0007-1

Abstract: derail factors and overturn factors of the train vehicles exceed the safety allowances, to which great attention

Keywords： rotational     offload     vertical     m/s     attention    

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Title Author Date Type Operation

Clean air captures attention whereas pollution distracts: evidence from brain activities

Journal Article

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural

Journal Article

Less attention paid to waterborne SARS-CoV-2 spreading in Beijing urban communities

Journal Article

Erratum to: Meter-scale variation within a single transect demands attention to taxon accumulation curves

Journal Article

Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine

Journal Article

Endothelial dysfunction in COVID-19 calls for immediate attention: the emerging roles of the endothelium

Weijian Hang, Chen Chen, Xin A. Zhang, Dao Wen Wang

Journal Article

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Efficient decoding self-attention for end-to-end speech synthesis

Wei ZHAO, Li XU

Journal Article

Attention-based encoder-decoder model for answer selection in question answering

Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI

Journal Article

Filter-cluster attention based recursive network for low-light enhancement

Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN,hzxcyanwind@163.com,lijinjiang@gmail.com-

Journal Article

Attention-based efficient robot grasp detection network

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Journal Article

EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

Yunzhan ZHOU, Tian FENG, Shihui SHUAI, Xiangdong LI, Lingyun SUN, Henry Been-Lirn DUH,yunzhan.zhou@durham.ac.uk,t.feng@zju.edu.cn

Journal Article

Progress in the research and development of p-xylene liquid phase oxidation process

WANG Lijun, CHENG Youwei, WANG Qinbo, LI Xi

Journal Article

Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

Journal Article

Dynamic response of a long span suspension bridge and running safety of a train under wind action

GUO Weiwei, XIA He, XU You-lin

Journal Article