BBC Verify has identified 254 incidents in which the Israeli military has been accused of killing or injuring people in Gaza, Lebanon, the occupied West Bank and Israel that it said it was investigating, but have no public outcome. Using purpose-built AI tools, BBC Verify combed through more than 60,000 news reports and examined hundreds of incidents in which the Israel Defense Forces (IDF) faced allegations of harm since the start of the Gaza war. Following extensive manual checking of each incident, we found 300 cases that the military said it was looking into, only 28 of which had outcomes published on the IDF website. For a further 18 incidents it was unclear if the investigation has been concluded. BBC Verify approached the IDF about each case multiple times but only received information on two incidents, which the IDF said are still under review.

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The IDF claims Palestinian soccer players it targeted during the Gaza war were allegedly terrorists for Hamas and Islamic Jihad, as FIFA rejects calls to suspend Israel.

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Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setting because they admit exact likelihood optimization, which is equivalent to minimizing the expected number of bits per message. However, conventional flows assume continuous data, which may lead to reconstruction errors when quantized for compression. For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data. As building blocks for IDFs, we introduce a flexible transformation layer called integer discrete coupling. Our experiments show that IDFs are competitive with other flow-based generative models. Furthermore, we demonstrate that IDF based compression achieves state-of-the-art lossless compression rates on CIFAR10, ImageNet32, and ImageNet64. To the best of our knowledge, this is the first lossless compression method that uses invertible neural networks.

In this paper, we capture the dependence among the labels using a learned tree-structured hierarchy.

Israeli strike on Yemen's Houthis reportedly kills eight The Israeli military says its air force has carried out its most powerful strike in Yemen in response to the Houthi movement's repeated drone and missile attacks on Israel. The Israel Defense Forces (IDF) said dozens of its aircraft bombed targets belonging to the Houthis' security and intelligence services, and military in the capital Sanaa. The Houthi-run government's health ministry denounced what it called Israel's brutal crime, saying civilian facilities and residential buildings were hit and that eight people were killed. It comes a day after 22 people were injured, two of them seriously, in a Houthi drone attack in the Israeli Red Sea resort of Eilat. The Houthis have controlled much of north-western Yemen since they ousted the country's internationally recognised government from there 10 years ago, sparking a civil war.

As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend of human-authored and machine-generated texts. Recent studies suggest that neural retrievers may exhibit a preferential inclination toward LLM-generated content, while classic term-based retrievers like BM25 tend to favor human-written documents. This paper investigates the influence of LLM-generated content on term-based retrieval models, which are valued for their efficiency and robust generalization across domains. Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity. These traits are aligned with LLMs being trained to optimize reader experience through diverse and precise expressions. Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries, rather than displaying an inherent source bias. This work provides a foundation for understanding and addressing potential biases in term-based IR systems managing mixed-source content.

The IDF said it had killed Izadi in a strike on an apartment in Qom, south of Tehran, in the early hours of Saturday. He had been in charge of the Palestine Corps of the Iranian Revolutionary Guards Corps's (IRGC) Quds Force, responsible for handling ties with the Palestinian armed groups. He was reportedly instrumental in arming and financing Hamas, and had been responsible for military co-ordination between senior IRGC commanders and Hamas leaders, the IDF said. In April 2024, Izadi narrowly survived an Israeli air strike targeting the Iranian consulate in Damascus, Syria - an attack that killed several high-ranking Quds Force commanders. Israel later on Saturday also claimed to have killed another Quds Force commander, Behnam Shahriyari in a drone strike as he was travelling in a car through western Iran.