Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem method to improve adversarial robustness of DNNs, which augments the training set with adversarial examples and adopts adversarial regularization loss to improve the robustness of DNNs. Existing adversarial training methods are facing the challenge to balance the accuracy and robustness.

Sub-headline: Beihang University researchers introduce a multi-authority ABE scheme, tackling dynamic access control challenges in cloud data.

A research team led by Dr. Sungjun Hong of the Photovoltaics Research Department at the Korea Institute of Energy Research (KIER) has developed a dual-molecule passivation technology that simultaneously improves the efficiency and stability of inverted perovskite solar cells. The research was conducted in collaboration with Professors Young Seok Park of Ulsan National Institute of Science and Technology (UNIST) and Professor Kyung-Koo Lee of Kunsan National University.