Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder

This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction measured by a portable EEG headset. In the authors’ previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE) for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.The 2017 International Conference on Artificial Life and Robotics(ICAROB 2017) , January 19 to 22, 2017, Seagaia Convention Center, Miyazaki, Japan

F-term Moduli Stabilization and Uplifting

We study K\"ahler moduli stabilization in IIB superstring theory. We propose a new moduli stabilization mechanism by the supersymmetry-braking chiral superfield which is coupled to K\"ahler moduli in K\"ahler potential. We also study uplifting of the Large Volume Scenario (LVS) by it. In both cases, the form of superpotential is crucial for moduli stabilization. We confirm that our uplifting mechanism does not destabilize the vacuum of the LVS drastically.Comment: 22 pages, 2 figure