At least one person has been killed and 18 others wounded in a Russian drone and missile attack on Ukraine, officials said, as Moscow launched its largest attack on its neighbour in weeks amid an ongoing diplomatic push for a ceasefire. Russian forces launched 574 drones and 40 missiles overnight, Ukraine's Air Force said on Thursday, adding that its air defence units had downed most of the attacks. But a number of the attacks struck targets in several locations across Ukraine, resulting in casualties and damage to buildings. In the western city of Lviv, about 70km (43 miles) from the border with Poland, a drone and missile attack killed one person, injured three and damaged 26 residential buildings, Governor Maksym Kozytskyi said. In Mukachevo, near the border with Hungary and Slovakia, 15 people were wounded in Russian attacks, local authorities said.

President Donald Trump says the U.S. will have to send more weapons to Ukraine, just days after Pentagon paused critical weapons deliveries to Kyiv. NATO jets were scrambled overnight as Russia carried out its largest drone attack yet on Ukraine, launching more than 700 drones, officials said. Ukrainian President Volodymyr Zelenskyy said the "new massive Russian attack on our cities" involved "728 drones of various types, including over 300 Shaheds, and 13 missiles – Kinzhals and Iskanders. "Most of the targets were shot down. Our interceptor drones were used -- dozens of enemy targets were downed, and we are scaling up this technology.

At least five people have been killed and more than 20 wounded as Russia launched a barrage of missiles, drones and bombs across Ukraine, officials said. The Ukrainian air force said on Saturday that Russia struck with 215 missiles and drones overnight, and Ukrainian air defences shot down and neutralised 87 drones and seven missiles. At least three people were killed and 17 others, including two children, were wounded in the northeastern city of Kharkiv, Mayor Ihor Terekhov said, describing the assault as "the most powerful" on the city since Russia launched its full-scale invasion of Ukraine in 2022. He reported 48 Iranian-made drones, two missiles and four guided bombs were fired before dawn at the city of 1.4 million people, located just 50km (30 miles) from the Russian border. "Drones are still circling above," Terekhov wrote on Telegram at 4:40am (01:40 GMT), as air raid sirens wailed across the city. Residential buildings and civilian infrastructure were heavily damaged.

This work presents the task of unsupervised location mapping, which seeks to map the trajectory of an individual narrative on a spatial map of locations in which a large set of narratives take place. Despite the fundamentality and generality of the task, very little work addressed the spatial mapping of narrative texts. The task consists of two parts: (1) inducing a ``map'' with the locations mentioned in a set of texts, and (2) extracting a trajectory from a single narrative and positioning it on the map. Following recent advances in increasing the context length of large language models, we propose a pipeline for this task in a completely unsupervised manner without predefining the set of labels. We test our method on two different domains: (1) Holocaust testimonies and (2) Lake District writing, namely multi-century literature on travels in the English Lake District. We perform both intrinsic and extrinsic evaluations for the task, with encouraging results, thereby setting a benchmark and evaluation practices for the task, as well as highlighting challenges.

Veteran and former intel officer Don Bramer joined Fox & Friends First to discuss his reaction to Trump tapping Keith Kellogg to be his Ukraine-Russia envoy and the Biden admin working with the Trump team on peace in the Middle East. Following an overnight missile and drone attack by Russia targeting Ukraine's key energy infrastructure, Russian President Vladimir Putin now says that government buildings in Kyiv could be targeted next using a new hypersonic missile that could also potentially reach the U.S. Russian attacks have not so far struck "decision-making centers" in the Ukrainian capital as Kyiv is heavily protected by air defenses. But Putin says Russia's Oreshnik hypersonic missile, which it fired for the first time at a Ukrainian city last week, is incapable of being intercepted. Russia fired the Oreshnik at the Ukrainian city of Dnipro on Nov. 21, striking a weapons production plant. This was in retaliation against Ukrainian strikes on a Russian military facility in Bryansk two days earlier with U.S. made long-range missiles called ATACMS, after President Biden had given Ukrainian President Volodymyr Zelenskyy permission to do so.

In this work, we propose Table-LLM-Specialist, or Table-Specialist for short, as a new self-trained fine-tuning paradigm specifically designed for table tasks. Our insight is that for each table task, there often exist two dual versions of the same task, one generative and one classification in nature. Leveraging their duality, we propose a Generator-Validator paradigm, to iteratively generate-then-validate training data from language-models, to fine-tune stronger \sys models that can specialize in a given task, without requiring manually-labeled data. Our extensive evaluations suggest that our Table-Specialist has (1) \textit{strong performance} on diverse table tasks over vanilla language-models -- for example, Table-Specialist fine-tuned on GPT-3.5 not only outperforms vanilla GPT-3.5, but can often match or surpass GPT-4 level quality, (2) \textit{lower cost} to deploy, because when Table-Specialist fine-tuned on GPT-3.5 achieve GPT-4 level quality, it becomes possible to deploy smaller models with lower latency and inference cost, with comparable quality, and (3) \textit{better generalizability} when evaluated across multiple benchmarks, since \sys is fine-tuned on a broad range of training data systematically generated from diverse real tables. Our code and data will be available at https://github.com/microsoft/Table-LLM-Specialist.

Russia has launched several strikes across Ukraine, killing at least five people and wounding several, in an attack that appeared to target energy infrastructure. Ukraine also launched a drone attack on Russia's central region of Saratov, injuring four. The exchange began around midnight on Sunday and continued beyond daybreak on Monday. Ukraine's air force reported multiple groups of Russian drones moving towards its eastern, northern, southern, and central regions, followed by numerous cruise and ballistic missiles. Authorities in at least six Ukrainian regions said blasts had been heard.

Are the longstanding robustness issues in NLP resolved by today's larger and more performant models? To address this question, we conduct a thorough investigation using 19 models of different sizes spanning different architectural choices and pretraining objectives. We conduct evaluations using (a) OOD and challenge test sets, (b) CheckLists, (c) contrast sets, and (d) adversarial inputs. Our analysis reveals that not all OOD tests provide further insight into robustness. Evaluating with CheckLists and contrast sets shows significant gaps in model performance; merely scaling models does not make them sufficiently robust. Finally, we point out that current approaches for adversarial evaluations of models are themselves problematic: they can be easily thwarted, and in their current forms, do not represent a sufficiently deep probe of model robustness. We conclude that not only is the question of robustness in NLP as yet unresolved, but even some of the approaches to measure robustness need to be reassessed.

Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning. Neural Module Networks(NMNs), follow the programmer-interpreter framework and design trainable modules to learn different reasoning skills. However, NMNs only have limited reasoning abilities, and lack numerical reasoning capability. We up-grade NMNs by: (a) bridging the gap between its interpreter and the complex questions; (b) introducing addition and subtraction modules that perform numerical reasoning over numbers. On a subset of DROP, experimental results show that our proposed methods enhance NMNs' numerical reasoning skills by 17.7% improvement of F1 score and significantly outperform previous state-of-the-art models.

The problem of synthesis in computer sciences, including cybernetics, artificial intelligence and system analysis, is analyzed. Main methods of realization this problem are discussed. Ways of search universal method of creation universal synthetic science are represented. As example of such universal method polymetric analysis is given. Perspective of further development of this research, including application polymetric method for the resolution main problems of computer sciences, is analyzed too.