Analyzing the noise robustness of deep neural networks

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples

Yupingfeng Pulvis Regulates the Balance of T Cell Subsets in Asthma Mice

Background. Yupingfeng Pulvis (HFBP) had played an active role in many diseases, especially respiratory tract infections. Exploring the possible prevention mechanism of HFBP may provide new ideas in clinical applications for this well-known herbal formula. Purpose. To study the possible mechanisms of therapy effect of HFBP on asthma mice via regulating the balance of Tregs and Th17 cells. Method. The female BALB/c mice were divided into five groups: control group, model group, prednisone (5.5 mg/kg) group, and 22 g/kg HFBP and 44 g/kg HFBP groups. Ovalbumin was used to make the asthma model of mice; the drug was ig administered daily after atomization for consecutive 15 d. The mice were killed after the last administration. The paraffin-embedded tissue sections of the lungs were stained by H&E. Tregs and Th17 cells in bronchoalveolar lavage fluid were detected by flow cytometry. IL-4, TGF-β, and TNF-α in the serum were detected by ELISA assay. Results. HFBP could alleviate the inflammation in the lung tissue of mice, decrease the proportion of Th17 cells, and increase the proportion of Treg cells in bronchoalveolar lavage fluid. HFBP could decrease IL-4 and TNF-α level and increase TGF-β level in blood. Conclusion. HFBP could treat the asthma through impacting the balance of Th17 cells and Treg cells as well as the levels of related inflammatory cytokines in asthma mice

Mesoscopic hysteretic model and parameter study on cemented sand and gravel material

According to the hysteresis loop characteristics of the cemented sand and gravel (CSG) material under an equal amplitude cyclic loading test, taking into account the influence of cycle times and plastic residual strain, a plastic mesoscopic hysteretic model of the CSG material was established based on the theoretical Preisach-Mayergoyz model. The sensitivity of the mechanical characteristics corresponding to the shape parameters, the fatigue life (frequency), the total number of hysteretic mesoscopic elastic units (HMEU), and the size of the load step in the model was analyzed. The results showed that the shape parameters mainly affect the form of the hysteresis loop curves in the loading and unloading sections, the total amount of HUEU affects the accuracy of expressing the mechanical properties of CSG materials, and the load step affects the fitting accuracy of the hysteresis loop curves. Based on the parameter analysis, the theoretical range of the mesoscopic parameters suitable for the mesoscopic hysteretic model of CSG are shown that, the shape parameter ξ is 1.2–3.0 and η is less than 0.3, the total number of HMEU is 500–10,000, the load step should not be significant when its value is greater than 0.08 MPa. The mesoscopic hysteretic model of CSG can also predict the fatigue life of CSG materials under constant amplitude cyclic loading. The research results can provide a theoretical basis for the dynamic characteristics and fatigue failure analysis of CSG materials