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Deep convolutional neural network has made great achievements in sea–land clutter classification for over-the-horizon radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea–land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea–land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network (SSGAN). Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting the standard adversarial loss of SSGAN and the joint feature matching loss. The classification performance of WL-SSGAN is evaluated on sea–land clutter datasets. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea–land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multi-task semi-supervised method. To sufficiently exploit massive unlabeled data, multi-task pseudo-labels and global to local self-correction strategy are proposed. Specifically, labeled images and massive amounts of unlabeled images with proposed multi-task pseudo-labels are leveraged for model optimization. The density level of the whole image is predicted in classification task. The density is estimated in density regression task. The crowd area is segmented out in segmentation task. To suppress incorrect predictions caused by the inevitable noises from some unlabeled data misleading the model, the counting relationship between classification task and density task is exploited to propose the global self-correction strategy, and the semantic consistency between density task and segmentation task is mined to propose the local self-correction strategy. The classification task and segmentation task contribute in generating the final highly refined density map from the density task. Extensive experiments on six benchmark datasets indicate the superiority of our method over the SOTA methods in semi-supervised paradigm. [ABSTRACT FROM AUTHOR]

Highlights: [•] FFSA system is designed from biomechanical information of human foot motion. [•] The sensing element conductance value is proportional to the force applied on it. [•] Foot–ground contact area formed by the edge sensing elements of ECA. [•] Feasibility and validity of FFSA system are experimentally tested in IPF system. [Copyright &y& Elsevier]

Abstract: In this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2 n ) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples. [Copyright &y& Elsevier]

Abstract: In this Letter, we have shown that the n-dimensional cellular neural network and delay cellular neural network can have not more than memory patterns, can have memory patterns which are locally exponentially stable. And we have obtained the estimates of attractive domain of such locally exponentially stable memory patterns. In addition, we have derived the conditions that the equilibrium point is locally exponentially stable when the equilibrium point locate the designated position. Some sufficient conditions have been obtained to guarantee the global exponential stability for the cellular neural networks. Those conditions can be directly derived from the parameters of the neural networks, are very easy to verified. The results presented in this Letter are the improvement and extension of the existed ones. Finally, the validity and performance of the results are illustrated by two simulation results. [Copyright &y& Elsevier]

Aims: Oxidative stress plays an important role in the development and progression of diabetic nephropathy (DN). We aimed to investigate if resveratrol (RSV) could ameliorate hyperglycemia-induced oxidative stress in renal tubules via modulating the SIRT1/FOXO3a pathway.Methods: The effects of RSV on diabetes rats were assessed by periodic acid-Schiff, Masson staining, immunohistochemistry, and western blot analyses. Additionally, oxidative indicators (such as catalase, superoxide dismutase, reactive oxygen species, and malondialdehyde), the deacetylase activity of SIRT1 and protein expressions of SIRT1, FOXO3a, and acetylated-FOXO3a were measured. These indicators were similarly evaluated in an in vitro study. Furthermore, the silencing of SIRT1 was used to confirm its role in the resistance to oxidative stress and the relationship between SIRT1 and FOXO3a in vitro.Results: After 16weeks of RSV treatment, the renal function and glomerulosclerosis of rats with DN was dramatically ameliorated. RSV treatment increased SIRT1 deacetylase activity, subsequently decreasing the expression of acetylated-FOXO3a and inhibiting the oxidative stress caused by hyperglycemia both in vivo and in vitro. The silencing of SIRT1 in HK-2 cells aggravated the high glucose-induced oxidative stress and overexpression of acetylated-FOXO3a; RSV treatment failed to protect against these effects.Conclusions: RSV modulates the SIRT1/FOXO3a pathway by increasing SIRT1 deacetylase activity, subsequently ameliorating hyperglycemia-induced renal tubular oxidative stress damage. This mechanism provides the basis for a new approach to developing an effective DN treatment, which is of great clinical significance for reducing the morbidity and mortality associated with DN. [ABSTRACT FROM AUTHOR]

A recent comment (Chang, 2014) theoretically demonstrated that for the linear system with the correlated process and measurement noises, the GASF framework proposed in the paper Wang et al. (2012) was equivalent to the conventional de-coupling filtering framework. In this note, we would further show that such equivalence between the two frameworks can be justified in a more general way, even for the nonlinear system. [ABSTRACT FROM AUTHOR]