It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult.That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missinglabel cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.

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In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is dynamic and new concepts may emerge in a data stream. In order to maintain a good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It has three functions: classify instances on currently known labels, detect the emergence of a new label, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. In addition, we show that MuENL can be easily extended to handle sparse high dimensional data streams by simply reducing the original dimensionality, and then applying MuENL on the reduced dimensional space. Our empirical evaluation shows the effectiveness of MuENL on several benchmark datasets and MuENLHD on the sparse high dimensional Weibo dataset.

The detection and counting of wheat ears are very important for crop field management, yield estimation, and phenotypic analysis. Previous studies have shown that most methods for detecting wheat ears were based on shallow features such as color and texture extracted by machine learning methods, which have obtained good results. However, due to the lack of robustness of these features, it was difficult for the above-mentioned methods to meet the detection and counting of wheat ears in natural scenes. Other studies have shown that convolutional neural network (CNN) methods could be used to achieve wheat ear detection and counting. However, the adhesion and occlusion of wheat ears limit the accuracy of detection. Therefore, to improve the accuracy of wheat ear detection and counting in the field, an improved YOLOv4 (you only look once v4) with CBAM (convolutional block attention module) including spatial and channel attention model was proposed that could enhance the feature extraction capabilities of the network by adding receptive field modules. In addition, to improve the generalization ability of the model, not only local wheat data (WD), but also two public data sets (WEDD and GWHDD) were used to construct the training set, the validation set, and the test set. The results showed that the model could effectively overcome the noise in the field environment and realize accurate detection and counting of wheat ears with different density distributions. The average accuracy of wheat ear detection was 94%, 96.04%, and 93.11%. Moreover, the wheat ears were counted on 60 wheat images. The results showed that R2 = 0.8968 for WD, 0.955 for WEDD, and 0.9884 for GWHDD. In short, the CBAM-YOLOv4 model could meet the actual requirements of wheat ear detection and counting, which provided technical support for other high-throughput parameters of the extraction of crops.

As the continuous changes in environmental regulations have a non-negligible impact on the innovation activities of micro subjects, and economic policy uncertainty has become one of the important influencing factors to be considered in the development of enterprises. Therefore, based on the panel data of Chinese high-tech enterprises from 2012–2017, this paper explores the impact of heterogeneous environmental regulations on firms’ green innovation from the perspective of economic policy uncertainty as a moderating variable. The empirical results show that, first, market-incentivized environmental regulation instruments have an inverted U-shaped relationship with innovation output, while voluntary environmental regulation produces a significant positive impact. Second, the U-shaped relationship between market-based environmental regulation and innovation output becomes more pronounced when economic policy uncertainty is high. However, it plays a negative moderating role in regulating the relationship between voluntary-based environmental regulation and innovation output. This paper not only illustrates the process of technological innovation by revealing the intrinsic mechanism of environmental regulation on firm innovation, but also provides insights for government in environmental governance from the perspective of economic policy uncertainty as well.

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The nonlinear dynamics of an actuator are considered during the output feedback control design of a quarter-car active suspension system with uncertainties. Because of the complexity of the suspension system with hydraulic actuator dynamics, a simple and effective sliding-mode strategy is employed to obtain both controller and observer. Instead of dividing the system into an actuator subsystem and a suspension subsystem, the system is repartitioned into a linear subsystem and a nonlinear subsystem, which facilitates controller design greatly. By specifying suitable sliding functions for the two subsystems respectively, and forcing the output of the nonlinear subsystem to track the desired fictitious input of the linear subsystem, the sliding-mode controller is created. By Lyapunov theory, robust stability is analyzed. For linear growth vanishing bounded uncertainties and nonvanishing bounded uncertainties, different observer forms are given to simplify the observer in different situations. Based on the constructed sliding-mode observer, the sliding-mode output feedback control suspension closed-loop system is accomplished. The convergence of observation error is subsequently proved. Simulation results verify the effect of the presented method.