He Wrote a Book About Antifa. Rutgers historian Mark Bray is trying to flee to Spain after an online campaign from far-right influencers was followed by death threats. He was turned back at the airport on his first attempt. A professor at Rutgers University who wrote a book about " antifa " almost a decade ago is trying--and struggling--to flee the US for Europe after a weeks-long online campaign against him by far-right influencers was followed by death threats. Mark Bray, a historian at Rutgers who specializes in Spanish history and radicalism, has been a far-right target ever since he published in 2017.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's 39th Conference on Artificial Intelligence (AAAI-25) was held in Philadelphia, Pennsylvania, on February 25 - March 4, 2025. TIKA is envisioned to create an open knowledge resource and serve as a hub for research, education and training on knowledge representation and knowledge engineering. Over 50 AI researchers convened at the workshop over two days. The discussions focused on different aspects of creating an open knowledge resource including foundational knowledge, automated reasoning, knowledge curation, education on knowledge axiomatization, and evaluation of outcomes. The opening discussion confirmed that the idea of curated knowledge, that is, knowledge captured in an expressive formal language that can be explicitly examined and verified by humans, is compelling. It must, however, be situated in the modern context of AI. Such a resource should address the limitations of existing generative ...

We examine a flexible algorithmic framework for solving monotone variational inequalities in the presence of randomness and uncertainty.

Tesla is being investigated by the US government after reports the firm's self-driving cars had broken traffic laws, including driving on the wrong side of the road and not stopping for red lights. It said it was aware of 58 reports where the electric cars had committed such violations, according to a filing from the National Highway Traffic Safety Administration (NHTSA). An estimated 2.9 million cars equipped with full self-driving tech will fall under the investigation. Tesla, whose boss Elon Musk recently became the world's first half-trillionaire, has been approached for comment. The NHTSA's preliminary evaluation will assess the scope, frequency, and potential safety consequences of the Full Self-Driving (Supervised) mode.

How Hong Kong gave rise to Labubu and a designer toy movement now shaping global culture. The following sentence might make a globalist cry out for joy: A toy that is manufactured by a Chinese company in Vietnamese factories, designed by a Dutch artist in Belgium, inspired by indie toy culture in Hong Kong, and made viral thanks to a Thai K-pop star, has turned into the biggest Gen-Z cultural trend of 2025. That abomination of a sentence is the story of Labubu, the creepy-cute stuffed monster that swept the world this summer. You must have seen the trend by now, but most people are still unaware of the global, decade-long story that led up to it. Last week, I published a feature story about my journey into the heart of Labubu, how this cultural mania moment was created, and where it may go from here.

We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces. As the geometric structure of the clusters (linear subspaces) forbids proper performance of general distance based approaches such as K -means, many model-specific methods have been proposed. In this paper, we provide new simple and efficient algorithms for this problem. Our statistical analysis shows that the algorithms are guaranteed exact (perfect) clustering performance under certain conditions on the number of points and the affinity between subspaces. These conditions are weaker than those considered in the standard statistical literature. Experimental results on synthetic data generated from the standard unions of subspaces model demonstrate our theory. We also show that our algorithm performs competitively against state-of-the-art algorithms on real-world applications such as motion segmentation and face clustering, with much simpler implementation and lower computational cost.