Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision-where only a limited number of labeled sentences are given and a large number of unlabeled sentences are available. Most existing work exploits unlabeled data based on the ideas of self-training (i.e., bootstrapping a model) and multi-view learning (e.g., ensembling multiple model variants). However, these methods either suffer from the issue of semantic drift, or do not fully capture the problem characteristics of relation extraction. In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentencetwo tasks are complementary to each other and can be optimized jointly for mutual enhancement. To model this intuition, we propose DualRE, a principled framework that introduces a retrieval module which is jointly trained with the original relation prediction module. In this way, high-quality samples selected by retrieval module from unlabeled data can be used to improve prediction module, and vice versa. Experimental results 1 on two public datasets as well as case studies demonstrate the effectiveness of the DualRE approach.

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Particle image velocimetry (PIV) is essential in experimental fluid dynamics. In the current work, we propose a new velocity field estimation paradigm, which is a synergetic combination of cross correlation and fully convolutional network (CC-FCN). Specifically, the fully convolutional network is used to optimize and correct a coarse velocity guess to achieve a super-resolution calculation. And the traditional cross correlation method provides the initial velocity field based on a coarse correlation with a large interrogation window. As a reference, the coarse velocity guess helps with improving the robustness of the proposed algorithm. CC-FCN has two types of input layers, one is for the particle images, and the other is for the initial velocity field calculated using cross correlation with a coarse resolution. First, two pyramidal modules extract features of particle images and initial velocity field, respectively. Then the fusion module appropriately fuses these features. Finally, CC-FCN achieves the super-resolution calculation through a series of deconvolution layers to obtain the single-pixel velocity field. As the supervised learning strategy is considered, synthetic data sets including ground-truth fluid motions are generated to train the network parameters. Synthetic and real experimental PIV data sets are used to test the trained neural network in terms of accuracy, precision, spatial resolution and robustness. The test results show that these attributes of CC-FCN are further improved compared with those of other tested PIV algorithms. The proposed model could therefore provide competitive and robust estimations for PIV experiments.

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