Why was El Paso airspace shut down? A new United States military laser-based anti-drone system led authorities to halt air traffic in and out of El Paso, Texas, after aviation officials raised serious concerns about risks to commercial aircraft. The Federal Aviation Administration (FAA) initially announced a 10-day airspace closure on Wednesday but removed the restriction less than eight hours later, a decision reports said stemmed from miscommunication between the Pentagon and aviation regulators. The FAA and the military had planned to discuss the issue at a February 20 meeting, but the army moved ahead without final FAA approval, prompting the agency to halt flights in El Paso, sources said. What happened when El Paso's airspace was shut down?

Construction uncertainties such as fabrication inaccuracies and material imperfections pose a significant challenge to contact-rich robotic manipulation by hindering precise and robust assembly. In this paper, we explore the performance and robustness of diffusion policy learning as a promising solution for contact-sensitive robotic assembly at construction scale, using timber mortise and tenon joints as a case study. A two-phase study is conducted: first, to evaluate policy performance and applicability; second, to assess robustness in handling fabrication uncertainties simulated as randomized perturbations to the mortise position. The best-performing policy achieved a total average success rate of 75% with perturbations up to 10 mm, including 100% success in unperturbed cases. The results demonstrate the potential of sensory-motor diffusion policies to generalize to a wide range of complex, contact-rich assembly tasks across construction and manufacturing, advancing robotic construction under uncertainty and contributing to safer, more efficient building practices.

Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

Guillermo del Toro Hopes He's Dead Before AI Art Goes Mainstream The director tells WIRED the real Victor Frankensteins are tyrannical politicians and Silicon Valley tech bros. Guillermo del Toro attends the Headline Gala screening of Netflix's during the 69th BFI London Film Festival. Guillermo del Toro loves a challenge. Nothing the 61-year-old director does could be termed "half-assed," and each of his movies is planned, scripted, and storyboarded with immense attention to detail. Such discipline is evident in, his adaptation of Mary Shelley's 1818 novel. It's a movie del Toro has been trying to make for years, and it shows. The elaborate sets and costumes--as well as some embellishing of Shelley's story--could only be the work of someone as connected as he is with his source material.

In Guillermo del Toro's "Frankenstein," a Vast Vision Gets Netflixed Down to Size The latest reanimation of Mary Shelley's classic tale, starring Oscar Isaac and Jacob Elordi, is a labyrinthine tour of a filmmaker's career-long obsessions. Earlier this year, Quentin Tarantino, when asked to parse the high points of his filmography in an interview, described the two-part "Kill Bill" (2003-04) as "the movie I was born to make." He added, "I think'Inglourious Basterds' is my masterpiece, but'Once Upon a Time . . . in Hollywood' is my favorite." Might these be distinctions without a difference? I'm generally wary of artistic-birthright narratives, not least because a filmmaker of remarkable talent, consistent vision, and good fortune might well wind up with multiple candidates for the honor.

The new president of the Los Angeles Police Commission, Teresa Sánchez-Gordon, is a former L.A. County judge who says her upbringing in an undocumented family gives her a unique perspective on the current challenges facing the LAPD.

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.

We introduce UniversalCEFR, a large-scale multilingual and multidimensional dataset of texts annotated with CEFR (Common European Framework of Reference) levels in 13 languages. To enable open research in automated readability and language proficiency assessment, UniversalCEFR comprises 505,807 CEFR-labeled texts curated from educational and learner-oriented resources, standardized into a unified data format to support consistent processing, analysis, and modelling across tasks and languages. To demonstrate its utility, we conduct benchmarking experiments using three modelling paradigms: a) linguistic feature-based classification, b) fine-tuning pre-trained LLMs, and c) descriptor-based prompting of instruction-tuned LLMs. Our results support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution for language proficiency research by standardising dataset formats, and promoting their accessibility to the global research community.

The use of Large Language Models (LLMs) has proven to be a tool that could help in the automatic detection of sexism. Previous studies have shown that these models contain biases that do not accurately reflect reality, especially for minority groups. Despite various efforts to improve the detection of sexist content, this task remains a significant challenge due to its subjective nature and the biases present in automated models. We explore the capabilities of different LLMs to detect sexism in social media text using the EXIST 2024 tweet dataset. It includes annotations from six distinct profiles for each tweet, allowing us to evaluate to what extent LLMs can mimic these groups' perceptions in sexism detection. Additionally, we analyze the demographic biases present in the models and conduct a statistical analysis to identify which demographic characteristics (age, gender) contribute most effectively to this task. Our results show that, while LLMs can to some extent detect sexism when considering the overall opinion of populations, they do not accurately replicate the diversity of perceptions among different demographic groups. This highlights the need for better-calibrated models that account for the diversity of perspectives across different populations.