Retired Air Force Gen. Charles Wald joins'Fox News Live' to weigh in on Russia's increased attacks on Ukraine despite President Donald Trump's ultimatum to Vladimir Putin. Russia and Ukraine took aim at corresponding Black Sea resort cities early Thursday morning, just hours after ceasefire talks in Turkey once again failed to deliver results. The major Russian resort city of Sochi was rocked by a Ukrainian drone strike that began around 1 a.m. and lasted until 3 a.m., where one person was reportedly killed and another injured, according to Ukrainian media outlet the Kyiv Independent, though the Ukrainian military has not commented on the incident. An oil depot in the Krasnodar Krai region where Sochi is located was also struck, though the extent of the damage remains unclear. Russia's President Vladimir Putin chairs a meeting via a video conference at the Kremlin in Moscow on July 23, 2025.

National Review editor-in-chief Rich Lowry and FOX Business' Liz Claman join'MediaBuzz' to discuss Hegseth's heated press conference where he called out the media's'hatred' of President Donald Trump. FIRST ON FOX: Defense Secretary Pete Hegseth has issued sweeping new orders to fast-track drone production and deployment, allowing commanders to procure and test them independently and requiring drone combat simulations across every branch of the military. As part of an aggressive push to outpace Russia and China in unmanned warfare, "the Department's bureaucratic gloves are coming off," Hegseth wrote. "Lethality will not be hindered by self-imposed restrictions... Our major risk is risk-avoidance." In a pair of memos first obtained by Fox News Digital, Hegseth rescinded legacy policies that he believes restricted innovation.

Past work has studied the effects of fine-tuning on large language models' (LLMs) overall performance on certain tasks. However, a quantitative and systematic method for analyzing its effect on individual outputs is still lacking. Here, we propose a new method for measuring the contribution that fine-tuning makes to individual LLM responses, assuming access to the original pre-trained model. Our method tracks the model's intermediate hidden states, providing a more fine-grained insight into the effects of fine-tuning than a simple comparison of final outputs from pre-trained and fine-tuned models. We introduce and theoretically analyze an exact decomposition of any fine-tuned LLM into a pre-training component and a fine-tuning component. Empirically, we find that model behavior and performance can be steered by up- or down-scaling the fine-tuning component during the forward pass. Motivated by this finding and our theoretical analysis, we define the Tuning Contribution (TuCo) as the ratio of the magnitudes of the fine-tuning component to the pre-training component. We observe that three prominent adversarial attacks on LLMs circumvent safety measures in a way that reduces TuCo, and that TuCo is consistently lower on prompts where these attacks succeed compared to those where they do not. This suggests that attenuating the effect of fine-tuning on model outputs plays a role in the success of such attacks. In summary, TuCo enables the quantitative study of how fine-tuning influences model behavior and safety, and vice versa.

Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme, with attractive mathematical properties, such as mean-field convergence results for non-convex loss functions. In this work, we study CBO in the context of closed-box adversarial attacks, which are imperceptible input perturbations that aim to fool a classifier, without accessing its gradient. Our contribution is to establish a connection between the so-called consensus hopping as introduced by Riedl et al. and natural evolution strategies (NES) commonly applied in the context of adversarial attacks and to rigorously relate both methods to gradient-based optimization schemes. Beyond that, we provide a comprehensive experimental study that shows that despite the conceptual similarities, CBO can outperform NES and other evolutionary strategies in certain scenarios.

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

--Unmanned aerial vehicles (UA Vs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UA Vs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. T o address this issue, we propose a deviation warning system for UA V landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, A verage Warning Delay (A WD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UA VLand-Data, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an A WD of 0.7 seconds with a deviation detection accuracy of 98.6%, demonstrating its effectiveness in enhancing UA V landing reliability. NMANNED aerial vehicles (UA Vs), also known as drones, have been widely used in fire detection, geological hazard monitoring, and dangerous behavior monitoring [1] for their agility, compactness, and cost-efficiency. To reduce the dependency of UA Vs on human labor and skills, UA V nests are widely used to minimize manual operations, allowing the UA Vs to perform autonomous monitoring. UA V nests also offer functionalities such as safe parking, charging, data transmission, routine maintenance, repairs, and communication relays [2].

Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial resolution, reliance on coastal station data, and poor generalization. Moreover, many prior models operate directly on unstructured spatial data, making them incompatible with modern deep learning architectures. In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green Blue (RGB)-encoded image representations, enabling the application of Convolutional Long Short Term Memory (ConvLSTM) networks for end-to-end spatiotemporal surge forecasting. Our model further integrates ground-truth wind fields as dynamic conditioning signals and topo-bathymetry as a static input, capturing physically meaningful drivers of surge evolution. Evaluated on a large-scale dataset of synthetic storms in the Gulf of Mexico, our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial extensibility to other coastal areas. By combining structured representation, physically grounded forcings, and scalable deep learning, this study advances the frontier of storm surge forecasting in usability, adaptability, and interpretability.

Sen. Tim Kaine, D-Va., tells Fox News Digital he's frustrated by US officials not being forthcoming about the drone incursions over Langley Air Force Base. FIRST ON FOX: A group of House Republicans is demanding details on how government agencies are addressing the growing threat of unauthorized drone incursions on U.S. military installations. In letters sent Thursday, the Subcommittee on Military and Foreign Affairs requested a trove of documents and communications from the Departments of Defense (DoD), Transportation (DOT), and Justice (DOJ). The letters note that in 2024 alone, there were 350 drone incursions at over 100 U.S. military bases. Lawmakers believe many of the responses to the illegal incursions, including an instance where a group of drones traipsed over Langley Air Force Base for over two weeks in December 2023, have been insufficient and fragmented.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Summer just started, and the first significant heat wave of the season is almost over. Some 265 million people across the Midwest and the eastern United States have experienced a week of temperatures in the 90s and triple digits, with a slew of all-time records set on Tuesday. While extreme heat waves can be caused by any number of factors, this particular one is thanks to a phenomenon called a heat dome: a ridge of atmospheric pressure that settles over a region like, well, a dome. Or, as the National Weather Service's Alex Lamers so wonderfully described it to NPR, think of it as a lid placed over a grilled cheese, which, as we all know, makes the cheese melt much faster.

Breakthroughs, discoveries, and DIY tips sent every weekday. The lifeblood of global communication flows through more than 807,800 miles worth of garden hose-wide cables woven across the sea floor. These cables, which reportedly transmit over 10 trillion worth of financial data every day, are vulnerable to extreme weather, decay, and, if recent reports are to be believed, acts of sabotage. The Associated Press estimates that at least 11 cables have been damaged since October 2023 in the Baltic Sea alone. Finnish and German authorities traced several of those incidents back to dragged anchors, which they allege may have been intentionally deployed to cause damage for political ends.