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

The DMRT gene family in amphioxus

<div><p>Doublesex and Mab-3-related transcription factor (DMRT) gene family is widely known for its involvement in sex determination and/or differentiation among different phyla. In this study, we identify eight DMRT genes in the cephalochordate amphioxus, a protochordate holding a key phylogenetic position. The eight DMRTs can be divided into two groups based on the conserved domain: BfDM044, BfDM045, BfDM55.1, BfDM115.1, and BfDM17.1 belong to the first group which have both DM and DMA domains, while BfDM246.1, BfDM084, and BfDM175 belong to the second group which have only DM domain. Most of the first group members have same genomic structure except BfDM17.1, while no regular pattern exists in the second group. Phylogenetic analysis of the DM domain sequences shows that DMRT genes in vertebrates form seven different independent clusters, and some even contain genes from invertebrates with high bootstrap. Notably, the first group members of amphioxus cluster with vertebrate DMRTs; while the second group members cluster into a single branch, which diverge from the vertebrate classes. The results suggest that several DMRT genes in vertebrates may evolve from homologous genes in invertebrates. As in nematode, drosophila, fish, and vertebrates, DMRT genes cluster is also found in amphioxus, which may be the result of gene duplication. Interspecific differences in the amphioxus DMRTs and sea squirt DMRTs may suggest post-speciation duplication of some DMRT genes.</p> </div

Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer

Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the performance of learning, our work intends to investigate the implicit regularization effects of the label noises, under mini-batch sampling settings of stochastic gradient descent (SGD), with assumptions that label noises are unbiased. Specifically, we analyze the learning dynamics of SGD over the quadratic loss with unbiased label noises, where we model the dynamics of SGD as a stochastic differentiable equation (SDE) with two diffusion terms (namely a Doubly Stochastic Model ). While the first diffusion term is caused by mini-batch sampling over the (labelnoiseless) loss gradients, as in many other works on SGD [1, 2], our model investigates the second noise term of SGD dynamics, which is caused by mini-batch sampling over the label noises, as an implicit regularizer. Our theoretical analysis finds such implicit regularizer would favor some convergence points that could stabilize model outputs against perturbation of parameters (namely inference stability). Though similar phenomenon have been investigated by Blanc et al. [3], our work doesn't assume SGD as an Ornstein-Uhlenbeck like process and achieve a more generalizable result with convergence of approximation proved. To validate our analysis, we design two sets of empirical studies to analyze the implicit regularizer of SGD with unbiased random label noises for deep neural networks training and linear regression. Our first experiment studies the noisy self-distillation tricks for deep learning, where student networks are trained using the outputs from well-trained teachers with additive unbiased random label noises. Our experiment shows that the implicit regularizer caused by the label noises tend to select models with improved inference stability

A FBG and Magnetostrictive Alloy based Magnetic Field Sensor with the Demodulation realized by Optoelectronic Oscillator

A FBG and magnetostrictive alloy based magnetic field optical sensor is realized with the demodulation by a new dual-loop optoelectronic oscillator (OEO). Unlike the previously reported OED-based sensing schemes, the stability of this proposed scheme is enhanced by using the combination of a fiber ring laser (FRL) cavity and a dual-loop OEO structure. The sensitivity of −48.40476 Hz/mT is obtained. The stability is up to 0.194 ppm

A potential biomarker hsa-miR-200a-5p distinguishing between benign thyroid tumors with papillary hyperplasia and papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common endocrine cancer with a significantly increase of the incidence recently. Several cytokines, such as thyroid peroxidase (TPO), cluster of differentiation 56 (CD56), Galectin-3, mesothelial cell (MC), cytokeratin 19 (CK19) and BRAF (B-raf) were recommended to be tested by immunohistochemistry (IHC) for a definitive diagnosis, but were still limited in clinical use because of their relative lower sensitivity and specificity. MicroRNA (miRNA), as a new molecular biomarkers, however, has not been reported yet so far. To address this, hsa-miR-200a-5p, a miRNA, was selected and detected in PTC patients by in situ hybrization with benign thyroid tumor with papillary hyperplasia as a control, and the differential expression of hsa-miR-200a-5p between fresh PTC tissues and control was detected by qRT-PCR. Expressive levels of cytokines of TPO, CD56, Galectin-3, MC, CK19 and B-raf were also detected by immunohistochemistry. The correlation was analyzed by SPSS software using Spearman methods. As expected, the hsa-miR-200a-5p expressive level was significantly increased in PTC patients, compared to that of control, and was consistent with that of TPO, CD56, Galectin-3, MC, CK19 and B-raf. In addition, expression of hsa-miR-200a-5p showed negative correlation to that of TPO (rs = - 0.734; **: P < 0.01) and CD56 (rs = - 0.570; **: P < 0.01), but positive correlation to that of Galectin-3 (rs = 0.601; **: P < 0.01), MC (rs = 0.508; **: P < 0.01), CK19 (rs = 0.712; **: P < 0.01) and B-raf (rs = 0.378; **: P < 0.01). PTC and papillary benign thyroid papillary hyperplasia are difficult to distinguish in morphology, so requiring immunohistochemistry to further differentiate the diagnosis, however, for the existing clinical common diagnostic marker for immunohistochemistry, the sensitivity and accuracy are low, it is easy to miss diagnosis. Therefore, there is an urgent need for a rapid and sensitive molecular marker. So miR-200a-5p can be used to assist in the diagnosis of PTC at the molecular level, and as a biomarker, can be effectively used to distinguish between PTC and benign thyroid tumor with papillary hyperplasia