Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversarial examples can be improved by attacking diverse models simultaneously, model augmentation methods which simulate different models by using transformed images are proposed. However, existing transformations for spatial domain do not translate to significantly diverse augmented models. To tackle this issue, we propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models. Specifically, we apply a spectrum transformation to the input and thus perform the model augmentation in the frequency domain. We theoretically prove that the transformation derived from frequency domain leads to a diverse spectrum saliency map, an indicator we proposed to reflect the diversity of substitute models. Notably, our method can be generally combined with existing attacks. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method, \textit{e.g.}, attacking nine state-of-the-art defense models with an average success rate of \textbf{95.4\%}. Our code is available in \url{https://github.com/yuyang-long/SSA}.Comment: Accepted by ECCV 202

Boosting Adversarial Attacks by Leveraging Decision Boundary Information

Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their gradients are different. Inspired by the fact that the decision boundaries of different models do not differ much, we conduct experiments and discover that the gradients of different models are more similar on the decision boundary than in the original position. Moreover, since the decision boundary in the vicinity of an input image is flat along most directions, we conjecture that the boundary gradients can help find an effective direction to cross the decision boundary of the victim models. Based on it, we propose a Boundary Fitting Attack to improve transferability. Specifically, we introduce a method to obtain a set of boundary points and leverage the gradient information of these points to update the adversarial examples. Notably, our method can be combined with existing gradient-based methods. Extensive experiments prove the effectiveness of our method, i.e., improving the success rate by 5.6% against normally trained CNNs and 14.9% against defense CNNs on average compared to state-of-the-art transfer-based attacks. Further we compare transformers with CNNs, the results indicate that transformers are more robust than CNNs. However, our method still outperforms existing methods when attacking transformers. Specifically, when using CNNs as substitute models, our method obtains an average attack success rate of 58.2%, which is 10.8% higher than other state-of-the-art transfer-based attacks

MC-Blur: A Comprehensive Benchmark for Image Deblurring

Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion and defocus. In this paper, we address how different deblurring methods perform in the case of multiple types of blur. For in-depth performance evaluation, we construct a new large-scale multi-cause image deblurring dataset (called MC-Blur), including real-world and synthesized blurry images with mixed factors of blurs. The images in the proposed MC-Blur dataset are collected using different techniques: averaging sharp images captured by a 1000-fps high-speed camera, convolving Ultra-High-Definition (UHD) sharp images with large-size kernels, adding defocus to images, and real-world blurry images captured by various camera models. Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios, analyze their efficiency, and investigate the built dataset's capacity. These benchmarking results provide a comprehensive overview of the advantages and limitations of current deblurring methods, and reveal the advances of our dataset

Follow-up of patients with COVID-19 by the Delta variant after hospital discharge in Guangzhou, Guandong, China

The B.1.617.2 (Delta) variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has contributed to a new increment in cases across the globe. We conducted a prospective follow-up of COVID-19 cases to explore the recurrence and potential propagation risk of the Delta variant and discuss potential explanations for the infection recurrence. A prospective, non-interventional follow-up of discharged patients who had SARS-CoV-2 infections by the Delta variant in Guangdong, China, from May 2021 to June 2021 was conducted. The subjects were asked to complete a physical health examination and undergo nucleic acid testing and antibody detection for the laboratory diagnosis of COVID-19. In total, 20.33% (25/123) of patients exhibited recurrent positive results after discharge. All patients with infection recurrence were asymptomatic and showed no abnormalities in the pulmonary computed tomography. The time from discharge to the recurrent positive testing was usually between 1-33 days, with a mean time of 9.36 days. The cycle threshold from the real-time polymerase chain reaction assay that detected the recurrence of positivity ranged from 27.48 to 39.00, with an average of 35.30. The proportion of vaccination in the non-recurrent group was higher than that in the recurrently positive group (26% vs. 4%; χ2 = 7.902; P < 0.05). Two months after discharge, the most common symptom was hair loss and 59.6% of patients had no long-term symptoms at all. It is possible for the Delta variant SARS-CoV-2 patients after discharge to show recurrent positive results of nucleic acid detection; however, there is a low risk of continuous community transmission. Both, the physical and mental quality of life of discharged patients were significantly affected. Our results suggest that it makes sense to implement mass vaccination against the Delta variant of SARS-CoV-2

Differential Responses of MET Activations to MET kinase Inhibitor and Neutralizing Antibody

Background: Aberrant MET tyrosine kinase signaling is known to cause cancer initiation and progression. While MET inhibitors are in clinical trials against several cancer types, the clinical efficacies are controversial and the molecular mechanisms toward sensitivity remain elusive. Methods: With the goal to investigate the molecular basis of MET amplification (MET amp ) and hepatocyte growth factor (HGF) autocrine-driven tumors in response to MET tyrosine kinase inhibitors (TKI) and neutralizing antibodies, we compared cancer cells harboring MET amp (MKN45 and MHCCH97H) or HGF-autocrine (JHH5 and U87) for their sensitivity and downstream biological responses to a MET-TKI (INC280) and an anti-MET monoclonal antibody (MetMab) in vitro, and for tumor inhibition in vivo. Results: We find that cancer cells driven by MET amp are more sensitive to INC280 than are those driven by HGF-autocrine activation. In MET amp cells, INC280 induced a DNA damage response with activation of repair through the p53BP1/ATM signaling pathway. Although MetMab failed to inhibit MET amp cell proliferation and tumor growth, both INC280 and MetMab reduced HGF-autocrine tumor growth. In addition, we also show that HGF stimulation promoted human HUVEC cell tube formation via the Src pathway, which was inhibited by either INC280 or MetMab. These observations suggest that in HGF-autocrine tumors, the endothelial cells are the secondary targets MET inhibitors. Conclusions: Our results demonstrate that MET amp and HGF-autocrine activation favor different molecular mechanisms. While combining MET TKIs and ATM inhibitors may enhance the efficacy for treating tumors harboring MET amp , a combined inhibition of MET and angiogenesis pathways may improve the therapeutic efficacy against HGF-autocrine tumors

Trend analysis and age-period-cohort effects on morbidity and mortality of liver cancer from 2010 to 2020 in Guangzhou, China

IntroductionLiver cancer is one of the most common malignant gastrointestinal tumors worldwide. This study intends to provide insight into the epidemiological characteristics and development trends of liver cancer incidence and mortality from 2010 to 2020 in Guangzhou, China.MethodsData were collected from the Cancer Registry and Reporting Office of Guangzhou Center for Disease Control and Prevention. Cross-sectional study, Joinpoint regression (JPR) model, and Age-Period-Cohort (APC) model were conducted to analyze the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) trend of liver cancer among the entire study period.ResultsThe age-standardized incidence and mortality of liver cancer in Guangzhou showed an overall decreasing trend. The disparity in risk of morbidity and mortality between the two sexes for liver cancer is increasing. The cohort effect was the most significant among those born in 1965~1969, and the risk of liver cancer incidence and mortality in the total population increased and then decreased with the birth cohort. Compared with the birth cohort born in 1950~1954 (the reference cohort), the risk of liver cancer incidence and mortality in the males born in 1995~1999 decreased by 32% and 41%, respectively, while the risk in the females decreased by 31% and 32%, respectively.ConclusionsThe early detection, prevention, clinical diagnosis, and treatment of liver cancer in Guangzhou have made remarkable achievements in recent years. However, the risk of liver cancer in the elderly and the middle-aged males is still at a high level. Therefore, the publicity of knowledge related to the prevention and treatment of liver cancer among the relevant population groups should be actively carried out to enhance the rate of early diagnosis and treatment of liver cancer and to advocate a healthier lifestyle

ASCAT Wind Superobbing Based on Feature Box

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing

Golgi protein 73 versus alpha-fetoprotein as a biomarker for hepatocellular carcinoma: a diagnostic meta- analysis

Abstract Backgrounds There have been conflicting reports about serum golgi protein 73 (GP73) as one of the most promising serum markers for the diagnosis of hepatocellular carcinoma (HCC). This study was to make a systematic review about the diagnostic accuracy of serum GP73 versus alpha-fetoprotein (AFP) for HCC. Methods After a systematic review of related studies, sensitivity, specificity and other measures about the accuracy of serum GP73 and AFP in the diagnosis of HCC were pooled using random-effects models. Summary receiver operating characteristic curve analysis was used to summarize the overall test performance. Results Eight studies were included in our meta-analysis. The summary estimates for serum GP73 and AFP in diagnosing HCC in the studies included were as follows: sensitivity, 76% (95% confidence interval (CI) 51-91%) vs. 70% (47-86%); specificity, 86% (95%CI 65-95%) vs. 89% (69-96%); diagnostic odds ratio (DOR), 18.59 (95%CI 5.33-64.91) vs. 18.00(9.41-34.46); and area under sROC, 0.88 (95%CI 0.77-0.99) vs. 0.86 (95%CI 0.84-0.87). Conclusions The current evidence indicates that serum GP73 has a comparable accuracy to AFP for the diagnosis of HCC, while the value of serum GP73 in combination with AFP for HCC detection deserves further investigation.</p