The Dirichlet problem for quasilinear elliptic differential equations in unbounded domains

AbstractThis paper is devoted to the second order, quasilinear elliptic Dirichlet problem of nondivergence type. We mainly consider the existence and uniqueness of classical solutions which radially converge at infinity under certain hypotheses

A Comprehensive Augmentation Framework for Anomaly Detection

Data augmentation methods are commonly integrated into the training of anomaly detection models. Previous approaches have primarily focused on replicating real-world anomalies or enhancing diversity, without considering that the standard of anomaly varies across different classes, potentially leading to a biased training distribution.This paper analyzes crucial traits of simulated anomalies that contribute to the training of reconstructive networks and condenses them into several methods, thus creating a comprehensive framework by selectively utilizing appropriate combinations.Furthermore, we integrate this framework with a reconstruction-based approach and concurrently propose a split training strategy that alleviates the issue of overfitting while avoiding introducing interference to the reconstruction process. The evaluations conducted on the MVTec anomaly detection dataset demonstrate that our method outperforms the previous state-of-the-art approach, particularly in terms of object classes. To evaluate generalizability, we generate a simulated dataset comprising anomalies with diverse characteristics since the original test samples only include specific types of anomalies and may lead to biased evaluations. Experimental results demonstrate that our approach exhibits promising potential for generalizing effectively to various unforeseen anomalies encountered in real-world scenarios

Exploring the Relationship between Samples and Masks for Robust Defect Localization

Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability.This paper proposes a one-stage framework that detects defective patterns directly without the modeling process.This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping.Experimental results on the texture class of the challenging MVTec AD dataset show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability

The effects of evidence type on online health headline selection – A moderation of thinking style

The acquisition of health information is conducive to promoting the public's health literacy and improving citizens' health. The display of online health information features an entering page that lists headlines hyperlinked to health article pages. Among the various techniques that help increase headline effectiveness, this study was particularly interested in evidence type (anecdotal type/numerical) and investigated how it influenced headline selection in the form of fixation and clicking and considered thinking styles as a possible moderator. Based on an eyetracking experiment, this study found that participants were more likely to click on numerical headline than anecdotal headline. In addition, message credibility had moderating effects on the relationship between evidence type and fixation and that between evidence type and clicking count. The findings provide useful implications for creating effective online headlines in the health domain and enrich our understanding of how information characteristics affect information selection

Adenosine deaminase acting on RNA 1 (ADAR1) as crucial regulators in cardiovascular diseases: structures, pathogenesis, and potential therapeutic approach

Cardiovascular diseases (CVDs) are a group of diseases that have a major impact on global health and are the leading cause of death. A large number of chemical base modifications in ribonucleic acid (RNA) are associated with cardiovascular diseases. A variety of ribonucleic acid modifications exist in cells, among which adenosine deaminase-dependent modification is one of the most common ribonucleic acid modifications. Adenosine deaminase acting on ribonucleic acid 1 (Adenosine deaminase acting on RNA 1) is a widely expressed double-stranded ribonucleic acid adenosine deaminase that forms inosine (A-to-I) by catalyzing the deamination of adenosine at specific sites of the target ribonucleic acid. In this review, we provide a comprehensive overview of the structure of Adenosine deaminase acting on RNA 1 and summarize the regulatory mechanisms of ADAR1-mediated ribonucleic acid editing in cardiovascular diseases, indicating Adenosine deaminase acting on RNA 1 as a promising therapeutic target in cardiovascular diseases

Protease‐Activatable Hybrid Nanoprobe for Tumor Imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108698/1/adfm201400419.pd