Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces.

Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks.

Chelate effects in glyme/lithium bis(trifluoromethanesulfonyl)amide solvate ionic liquids. I. Stability of solvate cations and correlation with electrolyte properties.

To develop a basic understanding of a new class of ionic liquids (ILs), "solvate" ILs, the transport properties of binary mixtures of lithium bis(trifluoromethanesulfonyl)amide (Li[TFSA]) and oligoethers (tetraglyme (G4), triglyme (G3), diglyme (G2), and monoglyme (G1)) or tetrahydrofuran (THF) were studied. The self-diffusion coefficient ratio of the solvents and Li(+) ions (Dsol/DLi) was a good metric for evaluating the stability of the complex cations consisting of Li(+) and the solvent(s). When the molar ratio of Li(+) ions and solvent oxygen atoms ([O]/[Li(+)]) was adjusted to 4 or 5, Dsol/DLi always exceeded unity for THF and G1-based mixtures even at the high concentrations, indicating the presence of uncoordinating or highly exchangeable solvents.