The accurate navigation of autonomous underwater vehicles critically depends on the precision of Doppler velocity log (DVL) velocity measurements. Recent advancements in deep learning have demonstrated significant potential in improving DVL outputs by leveraging spatiotemporal dependencies across multiple sensor modalities. However, integrating these estimates into model-based filters, such as the extended Kalman filter, introduces statistical inconsistencies, most notably, cross-correlations between process and measurement noise. This paper addresses this challenge by proposing a cross-correlation-aware deep INS/DVL fusion framework. Building upon BeamsNet, a convolutional neural network designed to estimate AUV velocity using DVL and inertial data, we integrate its output into a navigation filter that explicitly accounts for the cross-correlation induced between the noise sources. This approach improves filter consistency and better reflects the underlying sensor error structure. Evaluated on two real-world underwater trajectories, the proposed method outperforms both least squares and cross-correlation-neglecting approaches in terms of state uncertainty. Notably, improvements exceed 10% in velocity and misalignment angle confidence metrics. Beyond demonstrating empirical performance, this framework provides a theoretically principled mechanism for embedding deep learning outputs within stochastic filters.

Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.

Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability. 1 Introduction El Ni no-Southern Oscillation (ENSO) is one of the most prominent modes of inter-annual climate variability, characterized by shifts in sea surface temperatures (SST) across the tropical Pacific Ocean and the weakening of equatorial trade winds.

United States officials are set to meet with their Ukrainian counterparts again after a round of talks with Russian negotiators on a partial ceasefire in Ukraine. A senior Ukrainian official told the AFP news agency that the meeting would be held later on Monday after US and Russian delegations wrap up their day's talks in Saudi Arabia's capital Riyadh. Monday's US-Russia talks were primarily focused on ending attacks on Black Sea shipping, with a view to ushering in a broader ceasefire agreement that would bring an end to the three-year Russia-Ukraine war. US officials had already met the Ukrainian team on Sunday to discuss the protection of civilian and energy infrastructure, said Ukrainian Defence Minister Rustem Umerov, who led the delegation and called the talks "productive". Reporting from Kyiv, Al Jazeera's Assed Baig said Ukraine was now keen to see Russia agree to a deal that would protect Black Sea shipping, particularly "the cessation of shelling of Ukrainian ports Odesa, Kherson and Mykolaiv".

Special Envoy to the Middle East Steve Witkoff tells'Hannity' what's next in Russia-Ukraine peace talks after President Donald Trump's phone call with Russian President Vladimir Putin. Peace talks between U.S. and Russian delegations aimed at ending the war in Ukraine are underway Monday in Saudi Arabia, according to media reports. The discussions come after Ukrainian President Volodymyr Zelenskyy said a delegation from his country had a "quite useful" meeting with an American team in Riyadh on Sunday. "Our team is working in a fully constructive manner, and the discussion is quite useful. The work of delegations continues. But no matter what we're discussing with our partners right now, Putin must be pushed to issue a real order to stop the strikes – because the one who brought this war must be the one to take it back," Zelenskyy said.

Visual Question Answering (VQA) requires reasoning across visual and textual modalities, yet Large Vision-Language Models (LVLMs) often lack integrated commonsense knowledge, limiting their robustness in real-world scenarios. To address this, we introduce MAGIC-VQA, a novel framework that enhances VQA by systematically integrating commonsense knowledge with LVLMs. MAGIC-VQA employs a three-stage process: (1) Explicit Knowledge Integration from external sources, (2) By-Type Post-Processing for contextual refinement, and (3) Implicit Knowledge Augmentation using a Graph Neural Network (GNN) for structured reasoning. While GNNs bring greater depth to structured inference, they enable superior relational inference beyond LVLMs. MAGIC-VQA bridges a key gap by unifying commonsensse knowledge with LVLM-driven reasoning, eliminating the need for extensive pre-training or complex prompt tuning. Our framework achieves state-of-the-art performance on benchmark datasets, significantly improving commonsense reasoning in VQA.

Air pollution has become a major threat to human health, making accurate forecasting crucial for pollution control. Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes, using either online or offline methods depending on whether fully integrated with meteorological models and run simultaneously. However, the high computational demands of both methods severely limit real-time prediction efficiency. Existing deep learning (DL) solutions employ online coupling strategies for global air pollution forecasting, which finetune pollution forecasting based on pretrained atmospheric models, requiring substantial training resources. This study pioneers a DL-based offline coupling framework that utilizes bilinear pooling to achieve offline coupling between meteorological fields and pollutants. The proposed model requires only 13% of the parameters of DL-based online coupling models while achieving competitive performance. Compared with the state-of-the-art global air pollution forecasting model CAMS, our approach demonstrates superiority in 63% variables across all forecast time steps and 85% variables in predictions exceeding 48 hours. This work pioneers experimental validation of the effectiveness of meteorological fields in DL-based global air pollution forecasting, demonstrating that offline coupling meteorological fields with pollutants can achieve a 15% relative reduction in RMSE across all pollution variables. The research establishes a new paradigm for real-time global air pollution warning systems and delivers critical technical support for developing more efficient and comprehensive AI-powered global atmospheric forecasting frameworks.

"I feel that he (Putin) wants peace," said President Trump's personal envoy Steve Witkoff, adding: "I think that you're going to see in Saudi Arabia on Monday some real progress." Yet Dmitry Peskov, the Kremlin spokesman has dampened expectations. "We are only at the beginning of this path," he told Russian state TV. Kyiv suffered one of its heaviest attacks from Russian drones on Saturday night, with three people killed, including a five-year-old girl. "We need to push Putin to give a real order to stop the strikes," said Ukraine's President Volodymyr Zelensky in his evening address on Sunday.

President Donald Trump's envoy to Russia and Ukraine says he doesn't believe Russian President Vladimir Putin wants to invade Europe. Envoy Steve Witkoff made the statement during a Sunday morning appearance on "Fox News Sunday," commenting on Putin's motives on a "larger scale." "Now I've been asked my opinion about what President Putin's motives are on a larger scale. And I simply have said that I just don't see that he wants to take all of Europe," Witkoff said. "This is a much different situation than it was in World War II. There was no NATO," he added.

At least seven people have been killed in overnight Russian drone attacks on the Ukrainian capital, as President Volodymyr Zelenskyy urged his Western allies to put more pressure on Moscow to cease its attacks on the country in advance of peace talks in Saudi Arabia. Three people, including a five-year-old, were killed and 10 were injured in a drone attack on Kyiv, the city's military administration said on Sunday. Elsewhere, four people were killed in Russian attacks in Donetsk region, regional Governor Vadym Filashkin said, including three who died in an attack on the front-line Ukrainian town of Dobropillya. Kyiv Mayor Vitali Klitschko wrote on Telegram that emergency services were dispatched to several city districts following fires and damage. Earlier, the country's air force said Russia launched 147 drones overnight on several Ukrainian regions.