Low-rank methods have earned high regard for solving problems of mixed denoising in hyperspectral images (HSI). However, for low-rank matrix/tensor-based denoising methods, high computational ...complexity and high tuning difficulty often accompany good results. To address this challenge, in this paper, we propose a tensor subspace low-rank learning method with a non-local prior to exploit the low-rankness of both spatial and spectral modes of an HSI tensor. Technically, the original noisy HSI tensor was first projected to a low-dimensional subspace. Then, an orthogonal tensor basis of subspace and a tensor coefficient were alternatively learned. The parameter-free non-local prior was enforced in the tensor subspace instead of in the original HSI tensor. Eventually, the t-linear representation of basis and coefficient tensors achieved the restoration of the latent clean low-rank tensor. The proposed method realizes complete tensor operations for subspace low-rank learning and avoids the correlation loss bought about by tensor flattening. Through comparing with the latest denoising methods by using several quantitative and qualitative indexes, extensive experiments conducted on two simulated and two real datasets have proved that the proposed method not only realizes the high accuracy of mixed denoising, but also remarkably improves the computational efficiency and usability in real applications.
Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial ...distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.
In this letter, using the sparse unmixing framework, a weighted collaborative sparse and <inline-formula> <tex-math notation="LaTeX">L_{1/2} </tex-math></inline-formula> low-rank regularization with ...superpixel segmentation method is proposed for hyperspectral unmixing. The method outlined here first uses superpixel segmentation to obtain local homogeneous regions. The reason for this approach is that the shape and size of superpixels are adaptive, which are better for obtaining homogeneous regions than square patches. Next, the weighted collaborative sparse term and <inline-formula> <tex-math notation="LaTeX">L_{1/2} </tex-math></inline-formula> low-rank regularization were utilized to exploit the spatial and spectral correlation of each superpixel. In addition, the smoothness between adjacent pixels is enforced by total variation regularization. Finally, the proposed method and several state-of-the-art methods were tested on two simulated data sets and two real data sets. The results demonstrate the superiority of the method proposed here.
Hyperspectral image (HSI) target detection plays a pivotal role in both military and civilian sectors. Nevertheless, this task is fraught with challenges because of the limited availability of target ...samples and the intricate nature of the background within real-world HSIs. In this study, we present an innovative background learning model based on the orthogonal subspace-guided variational autoencoder (VAE), tailored to discern the background distribution in hyperspectral imagery. Given the scarcity of target samples, our model is exclusively trained on background spectral samples, enabling precise modeling of the background distribution. The crux of our approach lies in detecting disparities between the reconstructed HSI and the original HSI, providing a mechanism for faithful target identification. To procure background samples, a coarse detection of the test HSI is first conducted. However, this process proves challenging, as obtaining pristine background pixels is a formidable task. To mitigate the influence of suspicious target samples on the background reconstruction, we employ orthogonal subspace loss on the reconstructed HSI. Extensive experiments conducted on four real-world HSIs substantiate that the proposed framework performs highly competitively and the results outperform other state-of-the-art HSI target detection methods. The source codes of this study are available at https://github.com/CX-He/OS-VAE .
Licochalcone A (LCA) is a flavonoid isolated from Glycyrrhiza uralensis Fisch that has shown promising therapeutic effects in various cancers. This study attempted to analyze its therapeutic ...potential for esophageal cancer (EC). Combining multiple databases and network pharmacology, we found that the mechanism of LCA inhibiting EC may be closely related to p53 signaling pathway, cell cycle regulation and apoptosis. Molecular docking was then used to predict the affinity between LCA and key targets. Subsequently, we selected three common EC cell lines for in vitro validation. LCA treatment significantly inhibited EC cell proliferation and colony formation. Wound healing and transwell assay showed that LCA can reduce the migration and invasion of EC cells, and down-regulated the expression of matrix metalloproteinases (MMP). LCA promoted excessive ROS production, decreased mitochondrial membrane potential, and upregulated the expression of Bax, Caspase3 and Caspase-9, all of which are involved in apoptosis. LCA treatment blocked the cell cycle in G2/M phase and decreased the expression of cyclin D1, cyclin B1, and CDK1. LCA significantly up-regulated p53 protein and gene expression, thereby inducing apoptosis and cycle arrest. Finally, the xenograft tumor model was established by subcutaneous injection of Eca-109 cells. LCA administration inhibited tumor growth by activating p53 signaling pathways and apoptosis. Meanwhile, there was no significant weight loss and few major organotoxicity and hematotoxicity. In conclusion, LCA is an excellent candidate for EC treatment by regulating p53 pathway to induce G2/M phase arrest and apoptosis.
Recently, tensor singular value decomposition (t-SVD) has demonstrated excellent performance in various high-dimensional information processing applications. However, in adapting t-SVD to handle the ...typical tensor data restoration tasks, such as hyperspectral image (HSI) denoising, the following questions remain inadequately addressed: 1) the existing tensor nuclear norm minimization (TNN) regime treats all tensor singular values alike; thus, it lacks flexibility and dominance in dealing with the sophisticated HSI tensor; 2) the existing t-SVD-based denoising methods cannot directly process order-<inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">p>3 </tex-math></inline-formula>) tensors; thus, they fail to comprehensively exploit the high-dimensional structural correlation of the HSI tensor along different modes. To address the above challenges, in this study, we first generalize a novel weighted order-<inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> TNN minimization regime, which integrates the adaptively reweighting strategy for matrix, third-order, and order-<inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> tensors in a unified architecture. Subsequently, an efficient subspace low-rank learning model is established, using HSI denoising tasks as an application example to corroborate the superiority of the proposed regime in approximating the high-dimensional low-rank structure of natural tensor data. Extensive experimental results substantiate that our effort surpasses existing state-of-the-art low-rank tensor recovery methods in both restoration accuracy and efficiency. The source code is available at https://github.com/CX-He/WTNN.git .
Load forecasting, as the baseline for decision-making, plays a key role in the management and control of the grid. Nevertheless, the rapid evolution of the smart grid has brought a dramatic increase ...in the volume of user-side data, traditional load forecasting approaches have to face the challenge of ensuring the accuracy of dynamic forecasting under the circumstance of the widespread application of big data. Meanwhile, the advance of the Industrial Internet of Things (IIoT) enables smart meters to acquire more plentiful data, which improves the accuracy of short-term load forecasting with appropriate utilization, but the gradual increase in the magnitude of data brought by IIoT technique has also left the computing equipment under great pressure. To address these challenges, in this paper, a Long Short-Term Memory(LSTM) network based short-term load forecasting approach deployed in the distributed cloud-edge environment, named Alice, is devised to deliver more precise results for the smart forecasting of power load. It adopts the LSTM network to perform the forecasting tasks and extends the whole system to the cloud-edge platform to implement parallel neural computing. Moreover, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm is invited to enhance the efficiency of LSTM. Eventually, experimental evaluations on real dataset elaborate the superiority of the proposed Alice approach when compared to other state-of-the-art approaches.
In this letter, using the sparse unmixing framework, a weighted collaborative sparse and Formula Omitted low-rank regularization with superpixel segmentation method is proposed for hyperspectral ...unmixing. The method outlined here first uses superpixel segmentation to obtain local homogeneous regions. The reason for this approach is that the shape and size of superpixels are adaptive, which are better for obtaining homogeneous regions than square patches. Next, the weighted collaborative sparse term and Formula Omitted low-rank regularization were utilized to exploit the spatial and spectral correlation of each superpixel. In addition, the smoothness between adjacent pixels is enforced by total variation regularization. Finally, the proposed method and several state-of-the-art methods were tested on two simulated data sets and two real data sets. The results demonstrate the superiority of the method proposed here.
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia and insulin resistance. Mung bean sprouts are traditionally considered a "folk" hypoglycemic food and ...their pharmacological effects and underlying mechanisms warrant further investigation. This study aimed to investigate the anti-diabetic effects of the exosomes-like nanoparticles in mung bean sprouts (MELNs) and explore the related molecular mechanisms. MELNs were isolated using a differential centrifugation-polyethylene glycol (PEG) method, and the identification of MELNs were confirmed by PAGE gel electrophoresis, agarose gel electrophoresis, thin-layer chromatography (TLC), and transmission electron microscopy (TEM). In the high-fat diet/streptozotocin (HFD/STZ) mouse model, MELNs ameliorated the progression of T2DM by increasing oral glucose tolerance test (OGTT) and insulin tolerance test (ITT) results, decreasing the fasting blood glucose level, and reducing the serum triglycerides (TG) and total cholesterol (TC). Histopathological examinations indicated MELNs diminished inflammatory infiltration of hepatocytes and amplified the area of islet B cells. In addition, MELNs decreased the oxidative stress levels in liver tissue and had good biocompatibility. In vitro experiments verified that MELNs improved the viability of glucosamine (GlcN) induced insulin-resistant hepatocytes. Furthermore, this study also revealed that MELNs upregulated GLUT4 & Nrf2 and down-regulated GSK-3beta via activating the PI3K/Akt signaling pathway, promoting the production of antioxidant enzymes, such as HO-1 and SOD, to reduce oxidative stress. MELNs mitigated the progression of type 2 diabetes in HFD/STZ mouse model. The underlying molecular mechanism is related to PI3K/Akt/GLUT4/GSK-3beta signaling pathway.
PDF
