Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet.

Phase retrieval(PR) problem is a kind of ill-condition inverse problem which can be found in various of applications. Utilizing the sparse priority, an algorithm called SWF(Sparse Wirtinger Flow) is proposed in this paper to deal with sparse PR problem based on the Wirtinger flow method. SWF firstly recovers the support of the signal and then updates the evaluation by hard thresholding method with an elaborate initialization. Theoretical analyses show that SWF has a geometric convergence for any k sparse n length signal with the sampling complexity O(k 2 logn). To get ε accuracy, the computational complexity of SWF is O(k 3 nlognlog 1 ε ). Numerical tests also demonstrate that SWF performs better than state-of-the-art methods especially when we have no priori knowledge about sparsity k. Moreover, SWF is also robust to the noise keywords: sparse phase retrieval, Wirtinger flow, gradient descent, hard thresholding

Phase retrieval(PR) problem is a kind of ill-condition inverse problem which is arising in various of applications. Based on the Wirtinger flow(WF) method, a reweighted Wirtinger flow(RWF) method is proposed to deal with PR problem. RWF finds the global optimum by solving a series of sub-PR problems with changing weights. Theoretical analyses illustrate that the RWF has a geometric convergence from a deliberate initialization when the weights are bounded by 1 and 10 9 . Numerical testing shows RWF has a lower sampling complexity compared with WF. As an essentially adaptive truncated Wirtinger flow(TWF) method, RWF performs better than TWF especially when the ratio between sampling number m and length of signal n is small.

Melatonin has been proposed as a potent anticarcinogen presents a short half-life for osteosarcoma (OS). Cell-in-cell (CIC) structures play a role in the development of malignant tumors by changing the tumor cell energy metabolism. This study developed a melatonin-loaded 3D printed magnesium–polycaprolactone (Mg–PCL) scaffold and investigated its effect and molecular mechanism on CIC in OS. Mg–PCL scaffold was prepared by 3D-printing and its characteristic was determined. The effect and molecular mechanism of Mg–PCL scaffold as well as melatonin-loaded Mg–PCL on OS growth and progression were investigated in vivo and in vitro. We found that melatonin receptor 1 (MT1) and CIC expressions were increased in OS tissues and cells. Melatonin treatment inhibit the key CIC pathway, Rho/ROCK, through the cAMP/PKA signaling pathway, interfering with the mitochondrial physiology of OS cells, and thus playing an anti-invasion and anti-metastasis role in OS. The Mg–PCL–MT could significantly inhibit distant organ metastasis of OS in the in vivo model. Our results showed that melatonin-loaded Mg–PCL scaffolds inhibited the proliferation, invasion and metastasis of OS cells through the CIC pathway. The Mg–PCL–MT could be a potential therapeutics for OS.