美國

The main goal of this study was to address a variety of topics concerning the role of the Ih current in HCN channels of SHANK Wild-Type and Knock-Out Thalamus Neurons (as described further below). This research explored the cellular effects of sedation (like Dexmedetomidine) and laser light stimulations on the Ih current of neurons, as well as discovering novel biomarkers for detecting Autism Spectrum Disorder. This study also showed that methods (like utilizing laser therapy with and without various photosensitizers) have the potential in raising depressed Ih currents of SHANK Knock-Out neurons.

We study the creation of physical adversarial examples, which are robust to real-world transformations, using a limited number of queries to the target black-box neural networks. We observe that robust models tend to be especially susceptible to foreground manipulations, which motivates our novel Foreground attack. We demonstrate that gradient priors are a useful signal for black-box attacks and therefore introduce an improved version of the popular SimBA. We also propose an algorithm for transferable attacks that selects the most similar surrogates to the target model. Our black-box attacks outperform state-of-the-art approaches they are based on and support our belief that the concept of model similarity could be leveraged to build strong attacks in a limited-information setting.

We study the creation of physical adversarial examples, which are robust to real-world transformations, using a limited number of queries to the target black-box neural networks. We observe that robust models tend to be especially susceptible to foreground manipulations, which motivates our novel Foreground attack. We demonstrate that gradient priors are a useful signal for black-box attacks and therefore introduce an improved version of the popular SimBA. We also propose an algorithm for transferable attacks that selects the most similar surrogates to the target model. Our black-box attacks outperform state-of-the-art approaches they are based on and support our belief that the concept of model similarity could be leveraged to build strong attacks in a limited-information setting.