Fox News chief national security correspondent Jennifer Griffin has the latest on Iran's claims of developing an advanced hypersonic missile on'Special Report.' United States officials believe Iran is sending Russia materials to build a drone manufacturing plant east of Moscow to produce more Iranian drones to use in Ukraine. The intelligence was made public by the National Security Council's Coordinator for Strategic Communications John Kirby on Friday. "As of May, Russia received hundreds of one-way attack [unmanned aerial vehicles], as well as UAV production-related equipment, from Iran," Kirby said. Russian President Vladimir Putin takes part in the ceremony of signing an agreement on the construction of the Rasht-Astara railway via a video link together with Iranian President Ebrahim Raisi, at the Kremlin in Moscow.

The wide accessibility of social media has provided linguistically under-represented communities with an extraordinary opportunity to create content in their native languages. This, however, comes with certain challenges in script normalization, particularly where the speakers of a language in a bilingual community rely on another script or orthography to write their native language. This paper addresses the problem of script normalization for several such languages that are mainly written in a Perso-Arabic script. Using synthetic data with various levels of noise and a transformer-based model, we demonstrate that the problem can be effectively remediated. We conduct a small-scale evaluation of real data as well. Our experiments indicate that script normalization is also beneficial to improve the performance of downstream tasks such as machine translation and language identification.

The Ukrainian Ministry of Defense (MOD) has shared footage of what appeared to be an air defense system destroying an object amid reports that Ukraine was able to shoot down more than 70 Russian cruise missiles in one day. Russian forces launched about 100 missiles on Ukraine Tuesday, Ukrinform reported, citing Ukrainian Air Force spokesperson Yuriy Ignat. Russia's previous heaviest attack was on Oct. 10, which involved 84 projectiles. Ukrainian air defenses were able to destroy 73 of the Russian cruise missiles, 10 Iranian-made Shahed 131 or 136 drones as well as one Orion unmanned aerial vehicle (UAV) during the most recent bombardment. "This is how our air defenses shot down 73 cruise missiles today," the MOD said in a post that included footage of what appeared to be an air defense system destroying an object.

Iran stands accused by Western powers of supplying drones to Russia for its war against Ukraine, with analysts saying such military cooperation is of immense interest for Tehran at a delicate moment for its theocratic leadership. The United States has denounced as "appalling" Russia's use of Iranian drones after residents of Kyiv and other cites were shaken by a spate of recent attacks. Ukraine has said around 400 Iranian drones have already been used against the civilian population of Ukraine, and Moscow has ordered around 2,000. Tehran has rejected the allegations. Iran and Russia, both former imperial powers who for centuries vied for domination of the Caspian Sea region, have long had a highly nuanced and delicate relationship marked by rivalry and cooperation.

The \textit{Petit Larousse illustr\'e} is a French dictionary first published in 1905. Its division in two main parts on language and on history and geography corresponds to a major milestone in French lexicography as well as a repository of general knowledge from this period. Although the value of many entries from 1905 remains intact, some descriptions now have a dimension that is more historical than contemporary. They are nonetheless significant to analyze and understand cultural representations from this time. A comparison with more recent information or a verification of these entries would require a tedious manual work. In this paper, we describe a new lexical resource, where we connected all the dictionary entries of the history and geography part to current data sources. For this, we linked each of these entries to a wikidata identifier. Using the wikidata links, we can automate more easily the identification, comparison, and verification of historically-situated representations. We give a few examples on how to process wikidata identifiers and we carried out a small analysis of the entities described in the dictionary to outline possible applications. The resource, i.e. the annotation of 20,245 dictionary entries with wikidata links, is available from GitHub url{https://github.com/pnugues/petit_larousse_1905/

It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as a MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and development of persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models shows 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.

Iran's top nuclear scientist woke up an hour before dawn, as he did most days, to study Islamic philosophy before his day began. That afternoon, he and his wife would leave their vacation home on the Caspian Sea and drive to their country house in Absard, a bucolic town east of Tehran, where they planned to spend the weekend. Iran's intelligence service had warned him of a possible assassination plot, but the scientist, Mohsen Fakhrizadeh, had brushed it off. Convinced that Fakhrizadeh was leading Iran's efforts to build a nuclear bomb, Israel had wanted to kill him for at least 14 years. But there had been so many threats and plots that he no longer paid them much attention. Despite his prominent position in Iran's military establishment, Fakhrizadeh wanted to live a normal life. And, disregarding the advice of his security team, he often drove his own car to Absard instead of having bodyguards drive him in an armored vehicle. It was a serious breach of security protocol, but he insisted. So shortly after noon on Friday, Nov. 27, he slipped behind the wheel of his black Nissan Teana sedan, his wife in the passenger seat beside him, and hit the road.

Iran's top nuclear scientist woke up an hour before dawn, as he did most days. That afternoon, he and his wife would leave their vacation home on the Caspian Sea and drive to their country house in Absard, east of Tehran. Convinced that Mohsen Fakhrizadeh was leading Iran's efforts to build a nuclear bomb, Israel had wanted to kill him for at least 14 years. Iran's intelligence had warned Fakhrizadeh of a possible assassination plot, but the scientist had brushed it off. So shortly after noon on November 27, he slipped behind the wheel of his black Nissan Teana sedan along with his wife and hit the road.

That afternoon, he and his wife would leave their vacation home on the Caspian Sea and drive to their country house in Absard, a bucolic town east of Tehran, where they planned to spend the weekend. Iran's intelligence service had warned him of a possible assassination plot, but the scientist, Mohsen Fakhrizadeh, had brushed it off. Convinced that Mr. Fakhrizadeh was leading Iran's efforts to build a nuclear bomb, Israel had wanted to kill him for at least 14 years. But there had been so many threats and plots that he no longer paid them much attention. Despite his prominent position in Iran's military establishment, Mr. Fakhrizadeh wanted to live a normal life. And, disregarding the advice of his security team, he often drove his own car to Absard instead of having bodyguards drive him in an armored vehicle. It was a serious breach of security protocol, but he insisted. So shortly after noon on Friday, Nov. 27, he slipped behind the wheel of his black Nissan Teana sedan, his wife in the passenger seat beside him, and hit the road. Since 2004, when the Israeli government ordered its foreign intelligence agency, the Mossad, to prevent Iran from obtaining nuclear weapons, the agency had been carrying out a campaign of sabotage and cyberattacks on Iran's nuclear fuel enrichment facilities.

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.