Recall (higher is better): the ratio of selected ID instances to the total number of ID instances.

ZIP Lab, Monash University, Australia We organize our supplementary material as follows: In section A, we provide a comprehensive explanation of extending PTQD to DDIM [10]. In section B, we show the statistical analysis of quantization noise. In section D, we provide additional visualization results on ImageNet and LSUN dataset. We first perform statistical tests to verify if the residual quantization noise adheres to a Gaussian distribution. This test is based on D'Agostino and Pearson's In Figure B, we present the variance of the residual uncorrelated quantization noise.

Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the "sign flipping probability" of random projections.

As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,

Artificial intelligence (AI) has been used to find two previously undiscovered possible cave entrances on the Moon, which could support human survival on future space missions. Daniel Le Corre, a PhD researcher at the University of Kent, surveyed less than 0.3% of the lunar surface before finding the two pits. The South Marius Hills Pit, which the university said was previously overlooked by researchers, is in an area thought to be rich in lava tubes, while the Bel'kovich A Pit is close to the Moon's north pole and more likely to be a source of water. The pits were detected using an AI model that was trained to scan publicly available Nasa images and identify pits based on their distinctive shape. The AI model is named Essa, which is short for entrances to sub-surface areas and a nod to the Cornish name of Mr Le Corre's hometown, Saltash.