This paper mainly discusses the asymptotic behaviors on the minimizers of convex processes. In view of the convexity argument, it is proved that the minimizers of convex processes with parameterized ...objective functions satisfy the functional moderate deviation principle. As some applications, the estimators in two basic models (threshold regression models and stochastic dynamical systems) are studied. In particular, the exponential convergence principles on the estimators converging to true parameters are proved.
Hyperspectral image (HSI) classification is an active research topic in remote sensing. Supervised learning-based methods have been widely used in HSI classification tasks due to their powerful ...feature extraction capabilities for cases of sufficiently labeled samples. However, practical applications often have limited samples with accurate labels due to the high cost of labeling or unreliable visual interpretation. We introduce a contrastive self-supervised learning (SSL) algorithm to achieve HSI classification for problems with few labeled samples. First, a new HSI-specific augmentation module is developed to generate sample pairs. Then, a contrastive SSL model based on Siamese networks is used to extract features from these easily accessible sample pairs. Finally, the labeled samples are taken to fine-tune the parameters of the classification model to boost classification performance. Tests of the contrastive self-supervised algorithm have been performed on two widely used HSI datasets. The experimental results reveal that the proposed algorithm requires a few labeled samples to achieve superior performance.
This paper investigates the problem of consensus for the multi-agent systems with uncertain perturbations. A novel adaptive–impulsive protocol is designed for reaching the consensus. Particularly, ...this protocol is only imposed on a small fraction of agents to pin the whole system to a prescribed value. Two consensus criteria are established in cases whether the underlying graph of the multi-agent system is strongly connected or not. Two numerical examples are given to demonstrate the effectiveness of the proposed criteria.
Deep neural networks (DNNs) have been widely used for hyperspectral image (HSI) classification. However, the superior performance of DNNs requires accurately labeled samples. In various applications, ...label noise in large-scale HSI data is often unavoidable and adversely affects the performance of a classifier. Therefore, establishing a robust classification model from an existing DNN classifier for the HSI dataset with label noise is a significant and challenging problem. In this report, we propose a meta-learning reweighting framework based on a joint positive and negative learning (JPNL) method which can adaptively reweight labeled samples to improve the robustness of the classification model. Experimental results on two commonly used hyperspectral imaging (HSI) datasets contaminated with label noise demonstrate significant improvements in classification performance and robustness achieved by the proposed framework, as compared to existing classifiers.
•Aiming at the classification of hyperspectral images with noise labels.•Focus on the enhancing the classification accuracy of meta-weight-net methods.•A joint algorithm combined by positive and negative reweighting learning is proposed.•The method makes full use of sample information without manually marking outliers.•Experimental results show that the algorithm significantly enhance the robustness.
Doxorubicin (DOX), a widely used powerful chemotherapeutic component for cancer treatment, can give rise to severe cardiotoxicity that limits its clinical use. Pyroptosis is characterized by ...proinflammation and has been defined as a new type of programmed cell death in recent years. However, whether the DOX-induced cardiotoxicity is related to pyroptosis, and if so, which genes are involved in this process is largely unknown. In this study, we sought to identify the effect of DOX on cardiomyocyte pyroptosis and further reveal the underlying regulatory mechanism.
In vitro and in vivo experiments showed that DOX treatment induced cardiomyocyte pyroptosis as evidenced by increased cell death and upregulated expression levels of NLR family pyrin domain containing 3 (NLRP3), caspase-3, IL-1β, IL-18 and GMDSD-N. Inhibition of NLRP3 rescued the DOX-induced pyroptosis. qRT-PCR showed that TINCR lncRNA was upregulated by DOX treatment and knockdown of TINCR reversed the DOX-induced pyroptosis both in vitro and in vivo. Mechanistic investigations revealed that TINCR increased NLRP3 level via recruiting IGF2BP1 to enhance NLRP3 mRNA. And the effect of TINCR on cardiomyocyte pyroptosis was attenuated by the inhibition of NLRP3 or IGF2BP1. Finally, TINCR was not involved in DOX-induced pyroptosis in cancer cells.
TINCR mediates the DOX-induced cardiotoxicity and pyroptosis in an IGF2BP1-dependent manner. Therefore, TINCR may serve as a promising therapeutic target to overcome the cardiotoxicity of chemotherapy for cancer therapy.
•DOX treatment induced cardiomyocyte pyroptosis.•TINCR lncRNA was upregulated by DOX treatment and knockdown of TINCR reversed the DOX-induced pyroptosis both in vitro and in vivo.•TINCR increased NLRP3 level via recruiting IGF2BP1 to enhance NLRP3 mRNA.
Contrastive learning has emerged as a promising technique for hyperspectral image (HSI) classification. However, the inherent limitation of sliding window sampling in HSI results in partial samples ...within a mini-batch exhibiting extremely high similarity. Consequently, there is an increased number of negative sample pairs composed of similar samples, significantly reducing the effectiveness of contrastive learning. Moreover, prevailing classification models heavily depends on convolutional operations, emphasizing the extraction of local features but struggle to capture long-distance dependencies in both spatial and spectral dimensions. To address these problems and fully leverage the abundance of unlabeled samples, we propose a novel purified contrastive learning (PCL) framework for HSI classification. We design a complementary spatial-spectral representation encoder architecture that combines convolutional neural network (CNN) and Transformer to capture local features and global dependencies. More importantly, a purified contrastive loss function is proposed based on super-pixel spatial prior. Extensive experiments on three public datasets demonstrate the superiority of PCL over state-of-the-art methods in HSI classification. The code for this work is available at https://github.com/zhaolin6/PCL for the sake of reproducibility.
Abstract Interface in perovskite solar cells (PSCs) is of vital importance because it dominates deep‐level defects and non‐radiative recombination, thus impacting both efficiency and stability ...further. Herein, a symmetrical acceptor–donor–acceptor (A–D–A) conjugated molecule with the core architecture of terthieno3,2‐b hiophene and 2‐(3‐oxo‐2,3‐dihydro‐1 H‐inden‐1‐ylidene)malononitrile, named 6TIC, as a versatile buffer layer, is adopted to enhance photovoltaic performance and stability simultaneously. It is found that the conjugated molecule filling at grain boundaries and surface can not only chemically anchor with perovskite components to substantially eliminate interfacial defects and suppress detestable non‐radiative recombination, but also effectively improve the energy level alignment and facilitate charge transfer efficiency at the interface, resulting in an excellent power conversion efficiency of 24.81% with an admirable fill factor of 84.5%. Furthermore, benefiting from the unexceptionable surface protection effect of the hydrophobic buffer layer, greatly improved operational stability is delivered, with retaining 90% of initial efficiency for 960 h aging in a relative humidity of 60 ± 5% air and 1450 h aging under continuous 85 °C heating stress. This strategy may provide a new avenue for advancing high‐efficiency and stable PSCs.
