MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification

Computer-aided diagnosis of skin diseases is an important tool. However, the interpretability of computer-aided diagnosis is currently poor. Dermatologists and patients cannot intuitively understand the learning and prediction process of neural networks, which will lead to a decrease in the credibility of computer-aided diagnosis. In addition, traditional methods need to be trained using negative samples in order to predict the presence or absence of a lesion, but medical data is often in short supply. In this paper, we propose a multiple high-order attention interaction model (MHA-UNet) for use in a highly explainable skin lesion segmentation task. MHA-UNet is able to obtain the presence or absence of a lesion by explainable reasoning without the need for training on negative samples. Specifically, we propose a high-order attention interaction mechanism that introduces squeeze attention to a higher level for feature attention. In addition, a multiple high-order attention interaction (MHAblock) module is proposed by combining the different features of different orders. For classifying the presence or absence of lesions, we conducted classification experiments on several publicly available datasets in the absence of negative samples, based on explainable reasoning about the interaction of 5 attention orders of MHAblock. The highest positive detection rate obtained from the experiments was 81.0% and the highest negative detection rate was 83.5%. For segmentation experiments, comparison experiments of the proposed method with 13 medical segmentation models and external validation experiments with 8 state-of-the-art models in three public datasets and our clinical dataset demonstrate the state-of-the-art performance of our model. The code is available from https://github.com/wurenkai/MHA-UNet

Phonon Effects on Spin-Charge Separation in One Dimension

Phonon effects on spin-charge separation in one dimension are investigated through the calculation of one-electron spectral functions in terms of the recently developed cluster perturbation theory together with an optimized phonon approach. It is found that the retardation effect due to the finiteness of phonon frequency suppresses the spin-charge separation and eventually makes it invisible in the spectral function. By comparing our results with experimental data of TTF-TCNQ, it is observed that the electron-phonon interaction must be taken into account when interpreting the ARPES data.Comment: 5 pages, 5 figures, minor differences with the published version in Physical Review Letter

Pyrrolidine Dithiocarbamate Attenuates Paraquat-Induced Lung Injury in Rats

Paraquat (PQ) has been demonstrated that the main target organ for the toxicity is the lung. This study aimed to investigate the potential protective effect of PDTC on the PQ-induced pulmonary damage. Fifty-four rats were divided into control, PQ-treated and PQ+PDTC-treated groups. Rats in the PQ group were administrated 40 mg/kg PQ by gastric gavage, and PDTC group with 40 mg/kg PQ followed by injection of 120 mg/kg PDTC (IP). On the days 3, 7, 14 and 21 after treatments, the activities of GSH-Px, SOD, MDA level and the content of HYP were measured. TGF-β1 mRNA and protein were assayed by RT-PCR and ELISA. MDA level in plasma and BALF was increased and the activities of GSH-Px and SOD were decreased significantly in the PQ-treated groups (P < .05) compared with control group. While the activities of GSH-Px and SOD in the PQ+PDTC-treated groups was markedly higher than that of PQ-treated groups (P < .05), and in contrast, MDA level was lower. TGF-β1 mRNA and protein were significantly lower in the PQ+PDTC-treated groups than that of PQ-treated groups (P < .05). The histopathological changes in the PQ+PDTC-treated groups were milder than those of PQ groups. Our results suggested that PDTC treatment significantly attenuated paraquat-induced pulmonary damage

Responses of microbial abundance and enzyme activity in integrated vertical-flow constructed wetlands for domestic and secondary wastewater

Although micro-organisms play a significant role in pollutant removal in constructed wetlands, little is known on the effect of wastewater-quality properties on microbial characteristics. In this study, two groups of integrated vertical-flow constructed wetland microcosms were applied to treat synthetic domestic wastewater and synthetic secondary effluent. The effects of wastewater-quality properties on microbial features were assessed. Results showed that higher values of microbial indicators were observed in the systems with domestic wastewater and in down-flow cells. Redundancy analysis revealed that organic matter concentration and temperature were two critical determinants influencing the microbial features

Reanalysis of the Gross-Llewellyn Smith sum rule up to O(αs4){\cal O}(\alpha_s^4)-order QCD corrections

In the paper, we reanalyze the properties of Gross-Llewellyn Smith (GLS) sum rule by using the $\mathcal{O}(\alpha_s^4)$-order QCD corrections with the help of principle of maximum conformality (PMC). By using the PMC single-scale approach, we obtain an accurate renormalization scale-and-scheme independent pQCD contribution for GLS sum rule, e.g. $S^{\rm GLS}(Q_0^2=3{\rm GeV}^2)|_{\rm PMC}=2.559^{+0.023}_{-0.024}$, where the error is squared average of those from $\Delta\alpha_s(M_Z)$, the predicted $\mathcal{O}(\alpha_s^5)$-order terms predicted by using the Pad\'{e} approximation approach. After applying the PMC, a more convergent pQCD series has been obtained, and the contributions from the unknown higher-order terms are highly suppressed. In combination with the nonperturbative high-twist contribution, our final prediction of GLS sum rule agrees well with the experimental data given by the CCFR collaboration.Comment: 6 pages, 5 figure