Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks

Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack success rates in white-box setting, more focus is shifted to black-box attacks, of which the transferability of adversarial examples has gained significant attention. In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success. But little work pays attention to its possible limitation. In this work, we observe that the deviation between the original gradient and the generated noise may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability. To this end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM). Specifically, we use data rescaling to substitute the sign function without extra computational cost. We further propose a Depth First Sampling method to eliminate the fluctuation of rescaling and stabilize the gradient update. Our method could be used in any gradient-based attacks and is extensible to be integrated with various input transformation or ensemble methods to further improve the adversarial transferability. Extensive experiments on the standard ImageNet dataset show that our method could significantly boost the transferability of gradient-based attacks and outperform the state-of-the-art baselines.Comment: 10 pages, 6 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2204.0288

Construction and validation of a cuproptosis-related prognostic model for glioblastoma

BackgroundCuproptosis, a newly reported type of programmed cell death, takes part in the regulation of tumor progression, treatment response, and prognosis. But the specific effect of cuproptosis-related genes (CRGs) on glioblastoma (GBM) is still unclear.MethodsThe transcriptome data and corresponding clinical data of GBM samples were downloaded from the TCGA and GEO databases. R software and R packages were used to perform statistical analysis, consensus cluster analysis, survival analysis, Cox regression analysis, Lasso regression analysis, and tumor microenvironment analysis. The mRNA and protein expression levels of model-related genes were detected by RT-qPCR and Western blot assays, respectively.ResultsThe expression profile of CRGs in 209 GBM samples from two separate datasets was obtained. Two cuproptosis subtypes, CRGcluster A and CRGcluster B, were identified by consensus cluster analysis. There were apparent differences in prognosis, tumor microenvironment, and immune checkpoint expression levels between the two subtypes, and there were 79 prognostic differentially expressed genes (DEGs). According to the prognostic DEGs, two gene subtypes, geneCluster A and geneCluster B, were identified, and a prognostic risk score model was constructed and validated. This model consists of five prognostic DEGs, including PDIA4, DUSP6, PTPRN, PILRB, and CBLN1. Ultimately, to improve the applicability of the model, a nomogram was established. Patients with GBM in the low-risk cluster have a higher mutation burden and predict a longer OS than in the high-risk group. Moreover, the risk score was related to drug sensitivity and negatively correlated with the CSC index.ConclusionWe successfully constructed a cuproptosis-related prognostic model, which can independently predict the prognosis of GBM patients. These results further complement the understanding of cuproptosis and provide new theoretical support for developing a more effective treatment strategy

Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems

Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications

Genomic selection analysis of morphological and adaptation traits in Chinese indigenous dog breeds

The significant morphological differences and abundant germplasm resources of Chinese indigenous dog breeds can be attributed to the diverse geographical environment, including plateaus, mountains, and a long history of raising dogs. The combination of both natural and artificial selection during the past several thousand years has led to hundreds of dog breeds with distinct morphological traits and environmental adaptations. China is one of the earliest countries to domesticate dogs and there are more than 50 ancient indigenous dog breeds. In this study, the run of homozygosity (ROH) and proportion of the autosomal genome covered by ROHs (FROH) were calculated for 10 dog breeds that are the most representative Chinese indigenous dogs based on 170K SNP microarray. The results of FROH showed that the Chuandong hound dogs (HCSSC) have the highest level of inbreeding among the tested breeds. The inbreeding in HCSSC occurred more recently than the Liangshan dogs (SCLSQ) dogs because of more numbers of long ROHs in HCSSC dogs, and the former also have higher inbreeding degree. In addition, there are significant differences in the inbreeding degree among different subpopulations of the same breed, such as the Thin dogs from Shaanxi and Shandong province. To explore genome-wide selection signatures among different breeds, including coat color, ear shape, and altitude adaptability, we performed genome selection analyses of FST and cross population extended haplotype homozygosity (XP-EHH). For the coat color, the FST analysis between Xiasi dogs (XSGZ) and HCSSC dogs was performed and identified multiple genes involved in coat color, hair follicle, and bone development, including MC1R, KITLG, SOX5, RSPO2, and TBX15. For the plateau adaptability, we performed FST and XP-EHH analyses between dogs from Tibet (Tibetan Mastiffs and Nyingchi dogs) and plain regions (Guangxi Biwei dogs GXBWQ and Guandong Sharpei dogs). The results showed the EPAS1 gene in dogs from Tibet undergo strong selection. Multiple genes identified for selection signals based on different usage of dogs. Furthermore, the results of ear shape analyses showed that MSRB3 was likely to be the main gene causing the drop ear of domestic dogs. Our study provides new insights into further understanding of Chinese indigenous dogs

Evidence for lunar tide effects in Earth’s plasmasphere

Tides are universal and affect spatially distributed systems, ranging from planetary to galactic scales. In the Earth–Moon system, effects caused by lunar tides were reported in the Earth’s crust, oceans, neutral gas-dominated atmosphere (including the ionosphere) and near-ground geomagnetic field. However, whether a lunar tide effect exists in the plasma-dominated regions has not been explored yet. Here we show evidence of a lunar tide-induced signal in the plasmasphere, the inner region of the magnetosphere, which is filled with cold plasma. We obtain these results by analysing variations in the plasmasphere’s boundary location over the past four decades from multisatellite observations. The signal possesses distinct diurnal (and monthly) periodicities, which are different from the semidiurnal (and semimonthly) variations dominant in the previously observed lunar tide effects in other regions. These results demonstrate the importance of lunar tidal effects in plasma-dominated regions, influencing understanding of the coupling between the Moon, atmosphere and magnetosphere system through gravity and electromagnetic forces. Furthermore, these findings may have implications for tidal interactions in other two-body celestial systems

Research on gas monitoring fusion of fixed place and mobile situation in coal mine underground

Existing gas monitoring of fixed place and mobile situation in coal mine underground is realized by safety monitoring system and intelligent gas inspection system separately. Monitoring data in the two systems has no relevance. Application status of the two systems was analyzed as well as existing problems. A viewpoint was proposed that data of the two systems could be fused, thus gas monitoring in fixed places and mobile gas detection in operation places could be supplementary to each other, so as to realize underground gas monitoring with no dead space. Main problems in data fusion of the two systems were researched, which were unifying monitoring data property, position information, data storage mode and data expression mode, and unifying monitoring data, time information and space information. Key technologies of the data fusion were also researched including main data management platform, accurate positioning of underground and a map of GIS