Kharg Island is a small rocky outcrop just 15 miles (24km) off the coast of Iran - but despite its size, it is one of the most critical pieces of Iran's energy infrastructure. US President Donald Trump recently posted on Truth Social that the US military had conducted what he called one of the most powerful bombing raids in the history of the Middle East, obliterating every military target on the island. BBC Security Brief's Mikey Kay breaks down why Kharg Island is a lifeline for Iran, and why it is so strategically important for the US. As turmoil continues in the Middle East, the price of crude oil rose to more than $100 a barrel on Monday. Watch: Ukrainian anti-drone'bullet' that could help defend Gulf The BBC's Diplomatic Correspondent, James Landale, is in Ukraine to see the latest anti-drone technology the country has to offer.

Meta is planning sweeping layoffs that could affect 20% or more of the company, three sources familiar with the matter told Reuters, as Meta seeks to offset costly artificial intelligence infrastructure bets and prepare for greater efficiency brought about by AI-assisted workers. No date has been set for the cuts and the magnitude has not been finalized, the people said. Top executives have recently signaled the plans to other senior leaders at Meta and told them to begin planning how to pare back, two of the people said. The sources spoke anonymously because they were not authorized to disclose the cuts. Meta did not immediately comment.

Such architectures impose hard constraintsonthemodel.

Is Dubai's glossy image under threat? Stephanie Baker had been celebrating her birthday with friends at a bar on Palm Jumeirah - Dubai's iconic man-made palm-shaped island lined with luxury hotels and beach clubs. But as the group stepped outside to head to another nearby venue, something unusual streaked across the night sky. Moments later, debris from a drone struck the five-star Fairmont hotel - Baker and her friends were standing right across the street. We all were scared, she says.

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The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them. In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.

We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination. Our main contribution is the design of the first sample near-optimal and almost linear-time algorithms with optimal error guarantees for both these problems. Specifically, for Gaussian robust mean estimation on Rd with contamination parameter ϵ (0,ϵ0) for a small absolute constant ϵ0, we give an algorithm with sample complexity n = O(d/ϵ2) and almost linear runtime that approximates the target mean within ℓ2-error O(ϵ). This improves on prior work that achieved this error guarantee with polynomially suboptimal sample and time complexity. For robust linear regression, we give the first algorithm with sample complexity n = O(d/ϵ2) and almost linear runtime that approximates the target regressor within ℓ2-error O(ϵ). This is the first polynomial sample and time algorithm achieving the optimal error guarantee, answering an open question in the literature. At the technical level, we develop a methodology that yields almost-linear time algorithms for multi-directional filtering that may be of broader interest.