The security of digital signatures has been a focus of research in emerging area. Threshold signature is a technique designed for distributed scenarios, allowing the signing key to be divided into multiple shares. This approach can resist single point failures of device and ensure key security. However, most existing surveys on threshold signature schemes are either not comprehensive or cover only a limited range of signature types.

Sub-headline: BIT researchers introduce Malcom to tackle cross-domain encrypted traffic detection using self-supervised learning.

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.

The team led by Prof. Gengtao Fu from Nanjing Normal University has successfully enhanced the electrocatalytic oxidation performance of 5-hydroxymethylfurfural (HMF) by employing a rare-earth Cerium (Ce) doping strategy to tune the geometric and electronic structure of a CuO catalyst. This catalyst achieves efficient conversion of HMF to FDCA with near-theoretical-limit Faradaic efficiency (98.4%) and high production rate. By enabling optimal configuration matching, it effectively reduces steric hindrance, offering a novel approach for biomass electrocatalytic conversion.

A universal potential for all-purpose atomic simulations has been pursued for decades, but remains challenging due to limitations in model expressiveness and dataset construction. Now, writing in the journal Science China Chemistry, the research team led by Professor Zhi-Pan Liu at Fudan University has developed a generalized global neural network potential (GG-NN) based on a newly proposed low-cost, high-accuracy High-order Pair-reduced Neural Network (HPNN) model and a comprehensive global potential energy surface dataset of 5.84 million configurations covering 83 elements. GG-NN achieves high-precision predictions for both energies and forces, offering the potential to significantly accelerate the prediction and design of molecular and materials systems.