'Animals are traumatised too': Pet rescuers under fire in Ukraine On a morning in February, animal shelter staff were getting changed for their shift when a Russian drone slammed into the centre of their compound in the frontline Ukrainian city of Zaporizhzhia. The steel door at the entrance probably saved their lives. More than a dozen animals sheltering at Give a Paw, Friend were not so lucky. It was terrifying, to put it mildly, says the group's head Iryna Didur. Residents rushed to help clean up the rubble and catch the animals that had escaped in terror.

Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg, MOCOv3) target primarily on representations at instance level and do not generalize well to dense prediction tasks, such as object detection and segmentation. Towards aligning SSL with dense predictions, this paper demonstrates for the first time the underlying mean-shift clustering process of Vision Transformers (ViT), which aligns well with natural image semantics (e.g., a world of objects and stuffs). By employing transformer for joint embedding and clustering, we propose a bi-level feature clustering SSL method, coined Feature-Level Self-supervised Learning (FLSL). We present the formal definition of the FLSL problem and construct the objectives from the mean-shift and k-means perspectives. We show that FLSL promotes remarkable semantic cluster representations and learns an encoding scheme amenable to intra-view and inter-view feature clustering. Experiments show that FLSL yields significant improvements in dense prediction tasks, achieving 44.9 (+2.8)% AP and 46.5% AP in object detection, as well as 40.8 (+2.3)%

A Justice Ministry panel discusses how the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence, at the ministry in Tokyo on Friday. An expert panel under the Justice Ministry has agreed that the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence. The agreement was made Friday, during the first meeting of the panel on civil compensation claims related to the unauthorized use of celebrities' images and voices by generative AI. The ministry is set to compile guidelines on the scope and standards for illegal acts under current law by this summer. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems. We also allow the data to be collected by a probabilistic policy as opposed to sampled uniformly from the population. This enables auditing to be conducted on data gathered for another purpose. Moreover, this policy may change over time and different policies may be used on different subpopulations. Finally, our methods can handle distribution shift resulting from either changes to the model or changes in the underlying population. Our approach is based on recent progress in anytime-valid inference and game-theoretic statistics--the "testing by betting" framework in particular. These connections ensure that our methods are interpretable, fast, and easy to implement. We demonstrate the efficacy of our approach on three benchmark fairness datasets.