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.

Real-Time Automated Solubility Screening Method Using Deep Neural Networks with Handcrafted Features.

Solubility measurements are essential in various research and industrial fields. With the automation of processes, the importance of automatic and real-time solubility measurements has increased. Although end-to-end learning methods are commonly used for classification tasks, the use of handcrafted features is still important for specific tasks with the limited labeled images of solutions used in industrial settings.

Mass spectrometric analysis of acid-assisted photochemical release of the trimethyl lock system on the monolayers on gold.

We report the acid-assisted photolysis of the trimethyl lock system which has long been harnessed for a variety of applications such as drug delivery, cellular imaging, enzyme activity assays, and surface patterning. By mass spectrometric analysis, we found that photoinduced intramolecular cyclization and the ensuing release of the pendant groups of the trimethyl lock on the self-assembled monolayers proceeded cleanly in the presence of HCl, to give a high yield.