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.

Arabic has diverse dialects, where one dialect can be substantially different from the others. In the NLP literature, some assumptions about these dialects are widely adopted (e.g., ``Arabic dialects can be grouped into distinguishable regional dialects") and are manifested in different computational tasks such as Arabic Dialect Identification (ADI). However, these assumptions are not quantitatively verified. We identify four of these assumptions and examine them by extending and analyzing a multi-label dataset, where the validity of each sentence in 11 different country-level dialects is manually assessed by speakers of these dialects. Our analysis indicates that the four assumptions oversimplify reality, and some of them are not always accurate. This in turn might be hindering further progress in different Arabic NLP tasks.

Event Causality Identification (ECI) has become a crucial task in Natural Language Processing (NLP), aimed at automatically extracting causalities from textual data. In this survey, we systematically address the foundational principles, technical frameworks, and challenges of ECI, offering a comprehensive taxonomy to categorize and clarify current research methodologies, as well as a quantitative assessment of existing models. We first establish a conceptual framework for ECI, outlining key definitions, problem formulations, and evaluation standards. Our taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification. For SECI, we examine feature pattern-based matching, deep semantic encoding, causal knowledge pre-training and prompt-based fine-tuning, and external knowledge enhancement methods. For DECI, we highlight approaches focused on event graph reasoning and prompt-based techniques to address the complexity of cross-sentence causal inference. Additionally, we analyze the strengths, limitations, and open challenges of each approach. We further conduct an extensive quantitative evaluation of various ECI methods on two benchmark datasets. Finally, we explore future research directions, highlighting promising pathways to overcome current limitations and broaden ECI applications.

US warships came under sustained missile and drone attack from Houthi fighters as they sailed off the coast of Yemen, the Pentagon has confirmed, with the armed group claiming it attacked the US aircraft carrier Abraham Lincoln and two US destroyers. Pentagon spokesperson Air Force Major General Patrick Ryder said on Tuesday that the United States military's Central Command (CENTCOM) forces "successfully repelled multiple Iranian backed Houthi attacks during a transit of the Bab al-Mandeb strait", which connects the Red Sea to the Gulf of Aden. Ryder told reporters at a news conference that two US-guided missile destroyers – the USS Stockdale and USS Spruance – were attacked by at least eight one-way attack drones, five antiship ballistic missiles and three antiship cruise missiles. All the Houthi drones and missiles "were successfully engaged and defeated", and neither of the US Navy ships were damaged or personnel hurt, he said. Ryder added that he was not aware of any attacks against the aircraft carrier USS Abraham Lincoln.

The United States military has struck a number of cities in Yemen, including the capital, Sanaa, and the key port city of Hodeidah. Forces from the US Central Command (CENTCOM), the military command responsible for US forces in the Middle East, "conducted strikes on 15 Houthi targets in Iranian-backed Houthi-controlled areas of Yemen today", it said on X on Friday. Four strikes targeted Sanaa and seven hit Hodeidah, according to the Houthi-run Al Masirah TV network. Correspondents with the AFP news agency also reported hearing loud explosions in both cities. The Hodeidah strikes hit the airport and the Katheib area, which has a Houthi-controlled military base, Al Masirah said.

Gen. Jack Keane joins'Fox Report' to discuss the escalating tensions in the Middle East amid fears of a wider war. While much of the world has eyes on Israel's battles with Hezbollah and Hamas, the U.S. Navy has its sights set on another of Iran's proxies, the Yemeni Houthi rebels. With a mission to keep international waterways at peace, the Navy now finds itself fending off attacks from the shadowy gang of pirates who have gone from arming themselves with assault rifles, pickup trucks and motorboats – to a seemingly unending supply of drones, missiles and other weaponry. The Houthis often attack unarmed Western ships carrying goods through the Red Sea and the Gulf of Aden – while the U.S. has responded in kind with drone attacks on Yemen. ISRAELI AIR FORCE STRIKES HOUTHI TARGETS IN YEMEN WITH'EXTENSIVE' OPERATION That's led to perilous waters along a trade route that typically sees some 1 trillion in goods pass through it, as well as shipments of aid to war-torn Sudan and the Yemeni people.

Yemen's Houthi group has claimed responsibility for the drone that struck overnight in Tel Aviv, Israel, killing one person and injuring eight. Israeli media identified the dead man as 50-year-old Yevgeny Ferder, who had moved to Israel from Belarus at the beginning of the Russia-Ukraine war. Last night's strike is unique -- it's the first time the group is known to have hit Tel Aviv, though the Houthi have waged a continued campaign against targets they claim are linked to Israel since the ongoing devastating war on Gaza broke out in October. The drone struck in central Tel Aviv in the early hours of Friday morning. The site itself is thought to be close to a number of hotels, many hosting those displaced from Israel's northern border with Lebanon. A US embassy office is also close to the site of the attack.

Yemen's Houthi fighters have claimed responsibility following a suspected drone attack on Israel's Tel Aviv, which killed one person and injured at least 10, according to reports. A spokesperson for the Houthi armed forces said in a post on social media on Friday that the Yemen-based group had "targeted'Tel Aviv' in occupied Palestine". The Israeli military said it had opened an investigation into the large explosion near the United States Embassy office in the city and would determine why the country's air defence systems were not activated to intercept the "aerial target". Israel's air force has increased patrols to "protect the country's skies", the military added in a post on social media. Israeli police said the body of a man was found in an apartment close to the explosion and that the circumstances were being investigated.

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024). NADI's objective is to help advance SoTA Arabic NLP by providing guidance, datasets, modeling opportunities, and standardized evaluation conditions that allow researchers to collaboratively compete on pre-specified tasks. NADI 2024 targeted both dialect identification cast as a multi-label task (Subtask~1), identification of the Arabic level of dialectness (Subtask~2), and dialect-to-MSA machine translation (Subtask~3). A total of 51 unique teams registered for the shared task, of whom 12 teams have participated (with 76 valid submissions during the test phase). Among these, three teams participated in Subtask~1, three in Subtask~2, and eight in Subtask~3. The winning teams achieved 50.57 F\textsubscript{1} on Subtask~1, 0.1403 RMSE for Subtask~2, and 20.44 BLEU in Subtask~3, respectively. Results show that Arabic dialect processing tasks such as dialect identification and machine translation remain challenging. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.