A l-cysteine functionalized magnetic hollow MnFe2O4 nanosphere material has been synthesised, with high magnetism, large interior cavities, and high porosity and surface activity. It has high adsorption efficiency and regenerated adsorption capacity for the removal of Cr6+ and Pb2+ in contaminated water.

In order to elucidate the effects of inclusions on the resistance to pitting corrosion of S32205 duplex stainless steel, a potentiodynamic anodic polarization test, a weight loss test, a SEM‐EDS analysis, an in situ analysis and ICP analysis are carried out. The results show that the (Ba, Cr, Si, Ca) oxides and (Mn, Si, Cr, Al) oxides are corroded more easily than (Cr, Mn, Al, Ti) oxides, and the equivalent diameter of the former is greater than that of the latter. The pits of dissolved inclusions act as the initial locations of pitting corrosion in the chlorine ions corrosion environment, which accelerates the corrosion of matrix. The proportion of matrix corrosion is higher than inclusions corrosion in the weight loss.

The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an MCMC influence function is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. The code is available at https: //github.com/fshp971/mcmc-unlearning.

The tremendous amount of accessible data in cyberspace face the risk of being unauthorized used for training deep learning models. To address this concern, methods are proposed to make data unlearnable for deep learning models by adding a type of error-minimizing noise. However, such conferred unlearnability is found fragile to adversarial training. In this paper, we design new methods to generate robust unlearnable examples that are protected from adversarial training. We first find that the vanilla error-minimizing noise, which suppresses the informative knowledge of data via minimizing the corresponding training loss, could not effectively minimize the adversarial training loss. This explains the vulnerability of error-minimizing noise in adversarial training. Based on the observation, robust error-minimizing noise is then introduced to reduce the adversarial training loss. Experiments show that the unlearnability brought by robust errorminimizing noise can effectively protect data from adversarial training in various scenarios. The code is available at https://github.com/fshp971/ robust-unlearnable-examples.