Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs

Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}.Comment: ICCV-2021 Code is availabe at https://github.com/hehao13/EBLNe

Palatini formulation of the R−1R^{-1}modified gravity with an additionally squared scalar curvature term

In this paper by deriving the Modified Friedmann equation in the Palatini formulation of $R^2$ gravity, first we discuss the problem of whether in Palatini formulation an additional $R^2$ term in Einstein's General Relativity action can drive an inflation. We show that the Palatini formulation of $R^2$ gravity cannot lead to the gravity-driven inflation as in the metric formalism. If considering no zero radiation and matter energy densities, we obtain that only under rather restrictive assumption about the radiation and matter energy densities there will be a mild power-law inflation $a(t)\sim t^2$, which is obviously different from the original vacuum energy-like driven inflation. Then we demonstrate that in the Palatini formulation of a more generally modified gravity, i.e., the $1/R+R^2$ model that intends to explain both the current cosmic acceleration and early time inflation, accelerating cosmic expansion achieved at late Universe evolution times under the model parameters satisfying $\alpha\ll\beta$.Comment: 14 pages, accepted for publication by CQ

EEG-based brain-computer interfaces are vulnerable to backdoor attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it

Extended Radio AGN at z ∼ 1 in the ORELSE Survey: The Confining Effect of Dense Environments

Recent hydrodynamic simulations and observations of radio jets have shown that the surrounding environment has a large effect on their resulting morphology. To investigate this, we use a sample of 50 Extended Radio Active Galactic Nuclei (ERAGN) detected in the Observations of Redshift Evolution in Large-Scale Environments survey. These sources are all successfully cross-identified to galaxies within a redshift range of 0.55 ≤ z ≤ 1.35, either through spectroscopic redshifts or accurate photometric redshifts. We find that ERAGN are more compact in high-density environments than those in low-density environments at a significance level of 4.5σ. Among a series of internal properties under our scrutiny, only the radio power demonstrates a positive correlation with their spatial extent. After removing the possible radio power effect, the difference of size in low- and high-density environments persists. In the global environment analyses, the majority (86%) of high-density ERAGN reside in the cluster/group environment. In addition, ERAGN in the cluster/group central regions are preferentially compact with a small scatter in size, compared to those in the cluster/group intermediate regions and fields. In conclusion, our data appear to support the interpretation that the dense intracluster gas in the central regions of galaxy clusters plays a major role in confining the spatial extent of radio jets

Approach to epigenetic analysis in language disorders

Language and learning disorders such as reading disability and language impairment are recognized to be subject to substantial genetic influences, but few causal mutations have been identified in the coding regions of candidate genes. Association analyses of single nucleotide polymorphisms have suggested the involvement of regulatory regions of these genes, and a few mutations affecting gene expression levels have been identified, indicating that the quantity rather than the quality of the gene product may be most relevant for these disorders. In addition, several of the candidate genes appear to be involved in neuronal migration, confirming the importance of early developmental processes. Accordingly, alterations in epigenetic processes such as DNA methylation and histone modification are likely to be important in the causes of language and learning disorders based on their functions in gene regulation. Epigenetic processes direct the differentiation of cells in early development when neurological pathways are set down, and mutations in genes involved in epigenetic regulation are known to cause cognitive disorders in humans. Epigenetic processes also regulate the changes in gene expression in response to learning, and alterations in histone modification are associated with learning and memory deficits in animals. Genetic defects in histone modification have been reversed in animals through therapeutic interventions resulting in rescue of these deficits, making it particularly important to investigate their potential contribution to learning disorders in humans

Expression analysis of the MCPH1/BRIT1 and BRCA1 tumor suppressor genes and telomerase splice variants in epithelial ovarian cancer.

Aims The aim of this study was to explore the correlation of hTERT splice variant expression with MCPH1/BRIT1 and BRCA1 expression in epithelial ovarian cancer (EOC) samples. Background Telomerase activation can contribute to the progression of tumors and the development of cancer. However, the regulation of telomerase activity remains unclear. MCPH1 (also known as BRIT1, BRCT-repeat inhibitor of hTERT expression) and BRCA1 are tumor suppressor genes that have been linked to telomerase expression. Methods qPCR was used to investigate telomerase splice variants, MCPH1/BRIT1 and BRCA1 expression in EOC tissue and primary cultures. Results The wild type α+/β+ hTERT variant was the most common splice variant in the EOC samples, followed by α+/β− hTERT, a dominant negative regulator of telomerase activity. EOC samples expressing high total hTERT demonstrated significantly lower MCPH1/BRIT1 expression in both tissue (p = 0.05) and primary cultures (p = 0.03). We identified a negative correlation between MCPH1/BRIT1 and α+/β+ hTERT (p = 0.04), and a strong positive association between MCPH1/BRIT1 and both α−/β+ hTERT and α−/β− hTERT (both p = 0.02). A positive association was observed between BRCA1 and α−/β+ hTERT and α−/β− hTERT expression (p = 0.003 and p = 0.04, respectively). Conclusions These findings support a regulatory effect of MCPH1/BRIT1 and BRCA1 on telomerase activity, particularly the negative association between MCPH1/BRIT1 and the functional form of hTERT (α+/β+)