Improving attention model based on cognition grounded data for sentiment analysis

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks

The Construction Of Monitoring And Warning System For Flash Flood Defense In China

Flash floods often occurred suddenly and they also usually induce the occurrence of landslides and debris flows. The work of flash flood defense is full of challenge because of the complex process of flash flood formation. Disasters caused by flash flood occurred frequently and have an increasing trend in China with huge casualty. Therefore the Chinese government increased more inputs on the study of flash flood defense. After some endeavors in recent years, a set of non-structural measures on flash flood defense has been established featuring Chinese characteristics. In these measures, monitoring and warning system is a core. This paper introduced basic situation of flash flood disaster in China firstly, and then focused on the introduction to the monitoring and warning system. This work could provide merits and references to other countries and regions facing flash flood problems

CTCF Mediates the Cell-Type Specific Spatial Organization of the Kcnq5 Locus and the Local Gene Regulation

Chromatin loops play important roles in the dynamic spatial organization of genes in the nucleus. Growing evidence has revealed that the multivalent functional zinc finger protein CCCTC-binding factor (CTCF) is a master regulator of genome spatial organization, and mediates the ubiquitous chromatin loops within the genome. Using circular chromosome conformation capture (4C) methodology, we discovered that CTCF may be a master organizer in mediating the spatial organization of the kcnq5 gene locus. We characterized the cell-type specific spatial organization of the kcnq5 gene locus mediated by CTCF in detail using chromosome conformation capture (3C) and 3C-derived techniques. Cohesion also participated in mediating the organization of this locus. RNAi-mediated knockdown of CTCF sharply diminished the interaction frequencies between the chromatin loops of the kcnq5 gene locus and down-regulated local gene expression. Functional analysis showed that the interacting chromatin loops of the kcnq5 gene locus can repress the gene expression in a luciferase reporter assay. These interacting chromatin fragments were a series of repressing elements whose contacts were mediated by CTCF. Therefore, these findings suggested that the dynamical spatial organization of the kcnq5 locus regulates local gene expression

Learning Heterogeneous Network Embedding From Text and Links

Finding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10 -9 , indicating very significant improvement

Daily streamflow simulation based on the improved machine learning method

Kan, G., He, X., Ding, L., Li, J., Hong, Y., Ren, M., Lei, T., Liang, K., Zuo, D., & Huang, P. (March-April, 2017). Daily streamflow simulation based on the improved machine learning method. Water Technology and Sciences (in Spanish), 8(2), 51-60. Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks

Research and Application of Reservoir Flood Control Optimal Operation Based on Improved Genetic Algorithm

This paper took the Foziling Reservoir in the Pi River Basin as an example, used an improved genetic algorithm to optimize the flood control dispatching during the flood process, and compared the results with the traditional genetic algorithm and the dispatching plan in the 2020 large-scale reservoir flood control operation plan. The results showed that, compared with the traditional genetic algorithm, the improved genetic algorithm saved the time for the model to determine the penalty coefficients and made the model application more convenient. At the same time, the design of the original scheduling scheme also has certain limitations. The scheduling results obtained by improving the genetic algorithm could occupy a small flood control capacity as much as possible under the premise of ensuring the safety of the reservoir itself and the downstream area

Not All Areas Are Equal: A Novel Separation-Restoration-Fusion Network for Image Raindrop Removal

© 2020 The Author(s) Computer Graphics Forum © 2020 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. Detecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image-to-image translation and propose a separation-restoration-fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi-scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state-of-the-art methods by a large margin on the benchmark datasets in extensive experiments

SCGA-Net: Skip Connections Global Attention Network for Image Restoration

© 2020 The Author(s) Computer Graphics Forum © 2020 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. Deep convolutional neural networks (DCNN) have shown their advantages in the image restoration tasks. But most existing DCNN-based methods still suffer from the residual corruptions and coarse textures. In this paper, we propose a general framework “Skip Connections Global Attention Network” to focus on the semantics delivery from shallow layers to deep layers for low-level vision tasks including image dehazing, image denoising, and low-light image enhancement. First of all, by applying dense dilated convolution and multi-scale feature fusion mechanism, we establish a novel encoder-decoder network framework to aggregate large-scale spatial context and enhance feature reuse. Secondly, the solution we proposed for skipping connection uses attention mechanism to constraint information, thereby enhancing the high-frequency details of feature maps and suppressing the output of corruptions. Finally, we also present a novel attention module dubbed global constraint attention, which could effectively captures the relationship between pixels on the entire feature maps, to obtain the subtle differences among pixels and produce an overall optimal 3D attention maps. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods in image dehazing, image denoising, and low-light image enhancement

Sponge City Construction in China: A Survey of the Challenges and Opportunities

Rapid urbanization in China has caused severe water and environmental problems in recent years. To resolve the issues, the Chinese government launched a sponge city construction program in 2015. While the sponge city construction initiative is drawing attention and is spreading fast nationwide, some challenges and risks remain. This study surveyed progress of all 30 pilot sponge cities and identified a broad array of challenges from technical, physical, regulatory, and financial, to community and institutional. The most dominant challenges involve uncertainties and risks. To resolve the issues, this study also identified various opportunities to improve China’s sponge city construction program. Based on the results, recommendations are proposed including urging local governments to adopt sponge city regulations and permits to alleviate water quality and urban pluvial flooding issues, fully measuring and accounting for economic and environmental benefits, embracing regional flexibility and results-oriented approaches, and focusing on a wider range of funding resources to finance the sponge city program. Coordination among other government agencies is critical, and this is true at all level of governments. Only through greater coordination, education, and broader funding could the sponge city program be advanced meaningfully and sustainably