Chinese word segmentation with local and global context representation learning

A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.

The breakfast imperative： The changing context of global food security

The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;

The Trends of Globalization and Digitalization are Changing the Market Contexts

We know that SME’s that trade online grow faster and create more jobs than those that only operate in their domestic markets.The Internet is breaking down many traditional barriers to global trade,but there is still much governments can do to speed and enable SME digitization and ecommerce.The opportunity is huge at

Rethinking Global Context in Crowd Counting

This paper investigates the role of global context for crowd counting.Specifically,a pure transformer is used to extract features with global information from overlapping image patches.Inspired by classification,we add a context token to the input sequence,to facilitate information exchange with tokens corresponding to image patches throughout transformer layers.Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions,we propose a token-attention module(TAM)to recalibrate encoded features through channel-wise attention informed by the context token.Beyond that,it is adopted to predict the total person count of the image through regression-token module(RTM).Extensive experiments on various datasets,including ShanghaiTech,UCFQNRF,JHU-CROWD++and NWPU,demonstrate that the proposed context extraction techniques can significantly improve the performanceover the baselines.

Intercultural Trust in Global Contexts:Synthesizing a Western Nomological Approach with a Chinese Systems Approach

Intercultural trust in global contexts plays a central role in helping people from different cultures to communicate comfortably,which is essential for cooperation.Attempting to construct a framework that might foster international cooperation,and thus be helpful for coping with global emergencies,we relate a Western nomological approach to an Eastern systems approach to analyse intercultural trust in global contexts.Considering cultural impacts on intercultural trust and the nomological framework of cultural differences,we propose an intercultural trust model to interpret how cultural differences influence trust.A qualitative study of Chinese-Irish interactions was conducted to interpret this model.We organized 10 seminars on intercultural trust,and interviewed 16 people to further explore the respondents'deeper feelings and experiences about intercultural trust in global contexts.Through this study,we have identified factors impacting on intercultural trust,and found that intercultural trust can be developed and improved in various ways.To llustrate these ways,we have provided tactics and methods for building intercultural trust in global contexts.Implications are highlighted for organizations to avoid cultural clashes and relevant political or economic risks.

Dense Face Network:A Dense Face Detector Based on Global Context and Visual Attention Mechanism

Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.

Improved Global Context Descriptor for Describing Interest Regions

The global context(GC) descriptor is improved for describing interest regions,uses gradient orientation for binning,and thus provides more robust invariance for geometric and photometric transformations.The performance of the improved GC(IGC) to image matching is studied through extensive experiments on the Oxford A?ne dataset.Empirical results indicate that the proposed IGC yields quite stable and robust results,signi?cantly outperforms the original GC,and also can outperform the classical scale-invariant feature transform(SIFT) in most of the test cases.By integrating the IGC to the SIFT,the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.

Document-Level Neural Machine Translation with Hierarchical Modeling of Global Context

Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global context for documentlevel neural machine translation(NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence.With this hierarchical architecture,we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.Notably,we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model.In addition,since large-scale in-domain document-level parallel corpora are usually unavailable,we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.

Natural Image Matting with Attended Global Context

Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.

The international conference on mountain development in a context of global change with special focus on the Himalayas was held successfully in Kathmandu, Nepal

The international conference on mountain development in a context of global change with special focus on the Himalayas was held in Kathmandu, Nepal on April 21-26.

Global video object segmentation with spatial constraint module

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.