Physical Adversarial Attack Meets Computer Vision: A Decade Survey.

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs' performance. This perplexing phenomenon not only exists in the digital space but also in the physical world.

Development of a real-time reverse transcription loop-mediated isothermal amplification method for the rapid detection of porcine epidemic diarrhea virus.

Background: Porcine epidemic diarrhea (PED) is an acute and highly contagious enteric disease characterized by severe enteritis, vomiting and watery diarrhea in swine. Recently, the outbreak of the epidemic disease has been a serious problem in swine industry. The objective of this study is to develop a rapid, sensitive, and real-time reverse transcription loop-mediated isothermal amplification (RT-LAMP) method for the detection of porcine epidemic diarrhea virus (PEDV) in less equipped laboratories.