Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russian drones on Wednesday hit a Ukrainian port city along the border with Romania, causing significant damage and a huge fire at facilities that are key to Ukrainian grain exports. The attacks followed the end of a deal with Russia that had allowed Ukrainian shipments to world markets from the Black Sea port of Odesa. Since scrapping the deal, Russia has hammered the country's ports with strikes, compounding the blow to the key industry.

Ukraine's coastal region of Odesa was rattled by Russian drones which hit grain storage facilities in the south of the region, according to authorities in Kyiv. The grain port of Izmail, an inland port across the Danube River from NATO-member Romania, was the main target of Moscow's drone attack. "As a result of the attack, fires broke out at the facilities of the port and industrial infrastructure of the region, and an elevator was damaged," Odesa region Governor Oleh Kiper said in a statement on the Telegram messaging app. Russia's continued attacks against the Ukrainian civilian infrastructure on #Danube, in the proximity of Romania, are unacceptable. These are war crimes and they further affect UA's capacity to transfer their food products towards those in need in the world.

The Russian military said anti-aircraft units thwarted a Ukrainian "terrorist attack" and downed drones targeting Moscow, but that one drone struck a high-rise tower that was hit earlier in the week. Russia said it had repelled an overnight Ukrainian drone attack aimed at its patrol boats in the Black Sea. Mykhailo Podolyak, an adviser to Ukrainian President Volodymyr Zelenskyy, told the Reuters news agency that Kyiv did not attack and would not attack civilian vessels in the Black Sea, calling Russian claims "fictitious". Drones struck populated areas in the Ukrainian city of Kharkiv, destroying two floors of a college dormitory, according to local officials. Kharkiv Mayor Ihor Terekhov said there had been three separate attacks on Ukraine's second-largest city.

Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model.

Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Prompt-to-Generate (AdaPTGen). The core insight of AdaPTGen is to adapt prompt templates of domain-specific knowledge into the model, which brings at least three benefits: (1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; (2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; (3) it allows us to design various tasks to explore the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy.

Ukraine has warned Vladimir Putin that more drone attacks coming -- just hours after a flying bot smashed into one of Moscow's skyscrapers for the second time in as many days. Although Kyiv refuses to officially take responsibility for such attacks inside Russia, this latest skirmish is considered to be part of a wider offensive aimed at shifting the focus of the conflict to the Kremlin's doorstep. Experts say the way Kyiv is looking to do this is with the help of drones in the air and by sea -- a'cheap', expendable technology which has been revolutionising modern warfare over the past two decades. It is certainly turning attention from the First World War-style trench warfare that has been raging throughout Ukraine since the conflict broke out - and there's a reason the rest of the world is watching. Here, MailOnline looks at how drones are changing the face of future conflict, and why Ukraine is ratcheting up the use of them in an attempt to win the propaganda war and turn the tide of Putin's invasion.

The common ancestor of Indo-European languages, which are now spoken by close to half the world's population, was spoken in the eastern Mediterranean around 8000 years ago, according to an analysis of related words. Indo-European languages, spanning from English to Sanskrit, have long been thought to share a common ancestor. The first linguist to make this link, William Jones, said in a lecture in 1786 that no linguist could examine Greek, Latin and Sanskrit together "without believing them to have sprung" from some common ancestor. But researchers have struggled to agree on the origin story of this so-called proto-Indo-European language, says Paul Heggarty, who is now at the Pontifical Catholic University of Peru. There are two main hypotheses, he says.

That may have been science fiction but scientists say the terrifying prophecy could soon become a reality. That's because new research warns that the Atlantic Ocean current which drives the Gulf Stream could collapse at'any time' from 2025 thanks to climate change. Known formally as the Atlantic Meridional Overturning Circulation (AMOC), the current is the driving force which brings warm water from the Gulf of Mexico up to the UK and is responsible for mild winters in Western Europe. If it collapsed, however, the impact would be devastating. Europe would be plunged into a deep freeze, while most of Africa, the Caribbean, and South American countries such as Colombia, Peru and Bolivia would experience rocketing temperatures.

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation underscores the importance of using a model architecture that supports the communication of prediction uncertainty and the effective integration of relevant, multi-modal features.

The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we introduce TreeFlow, the tree-based approach that combines the benefits of using tree ensembles with the capabilities of modeling flexible probability distributions using normalizing flows. The main idea of the solution is to use a tree-based model as a feature extractor and combine it with a conditional variant of normalizing flow. Consequently, our approach is capable of modeling complex distributions for the regression outputs. We evaluate the proposed method on challenging regression benchmarks with varying volume, feature characteristics, and target dimensionality. We obtain the SOTA results for both probabilistic and deterministic metrics on datasets with multi-modal target distributions and competitive results on unimodal ones compared to tree-based regression baselines.