Deep neural networks (DNNs) are highly susceptible to adversarial samples, raising concerns about their reliability in safety-critical tasks. Currently, methods of evaluating adversarial robustness are primarily categorized into attack-based and certified robustness evaluation approaches. The former not only relies on specific attack algorithms but also is highly time-consuming, while the latter due to its analytical nature, is typically difficult to implement for large and complex models. A few studies evaluate model robustness based on the model’s decision boundary, but they suffer from low evaluation accuracy. To address the aforementioned issues, we propose a novel adversarial robustness evaluation metric, Robustness Difference Index (RDI), which is based on model statistical features. RDI draws inspiration from clustering evaluation by analyzing the intra-class and inter-class distances of feature vectors separated by the decision boundary to quantify model robustness. It is attack-independent and has high computational efficiency. Experiments show that, RDI demonstrates a stronger correlation with the gold-standard adversarial robustness metric of attack success rate (ASR). The average computation time of RDI is only 1/30 of the evaluation method based on the PGD attack. Our open-source code is available at: https://github.com/BUPTAIOC/RDI.

Abstract. The [Formula: see text]-blow-up of a given graph is obtained by replacing each edge by a clique of order [Formula: see text] where the new vertices of the cliques are distinct. Liu and Yuan determined the extremal graphs for the 3-blow-ups of a triangle and the [Formula: see text]-blow-ups of any complete graph with order at most [Formula: see text], respectively. We determine the Turán number for the [Formula: see text]-blow-ups of a complete graph with order at least [Formula: see text], completing the study of the extremal graphs for [Formula: see text]-blow-ups of complete graphs.

Helicobacter pylori (H. pylori) infection remains a cause of significant morbidity and mortality worldwide. Comprehensive understanding of the pathogenic mechanism of H. pylori and its interaction with host will contribute to developing novel prophylactical and therapeutical strategies. Here, we first determined microRNA (miRNA) levels in H. pylori-infected patients with gastritis, duodenal ulcer, gastric cancer or mucosa-associated lymphoid tissue lymphoma using miRNA data sets. Thirty-four differentially expressed miRNAs were identified and functional enrichment analysis of those miRNA target genes revealed that H. pylori infection were strongly associated with pathway in cancer and regulation of mRNA synthesis. Using disease connectivity analysis of 28 hub genes, we found that H. pylori may increase the risk of many extragastric diseases (e.g. cardiovascular disease, hemic and lymphatic diseases and nervous system disease). Altogether, our integrated analysis provided a new method to predict pathogen-human disease connectivity based on miRNA-mRNA interaction network and indicated anti-H. pylori therapy as an effective means of human diseases prevention.

How muscles are used is a key to understanding the internal driving of fish swimming. However, the underlying mechanisms of some features of the muscle activation patterns and their differential appearance in different species are still obscure. In this study, we explain the muscle activation patterns by using 3D computational fluid dynamics models coupled to the motion of fish with prescribed deformation and examining the torque and power required along the fish body with two primary swimming modes. We find that the torque required by the hydrodynamic forces and body inertia exhibits a wave pattern that travels faster than the curvature wave in both anguilliform and carangiform swimmers, which can explain the traveling wave speeds of the muscle activations. Notably, intermittent negative power (i.e., power delivered by the fluid to the body) on the posterior part, along with a timely transfer of torque and energy by tendons, explains the decrease in the duration of muscle activation towards the tail. The torque contribution from the body elasticity further clarifies the wave speed increase or the reverse of the wave direction of the muscle activation on the posterior part of a carangiform swimmer. For anguilliform swimmers, the absence of the aforementioned changes in the muscle activation on the posterior part is consistent with our torque prediction and the absence of long tendons from experimental observations. These results provide novel insights into the functions of muscles and tendons as an integral part of the internal driving system, especially from an energy perspective, and they highlight the differences in the internal driving systems between the two primary swimming modes.