Earlier this month, the US military said the Unity Explorer, sailing under the flag of the Bahamas and owned by a British company, was among three commercial vessels targeted in an attack by Iranian-backed Houthi rebels.

Recent studies have shown that deep learning (DL) models can skillfully predict the El Ni\~no-Southern Oscillation (ENSO) forecasts over 1.5 years ahead. However, concerns regarding the reliability of predictions made by DL methods persist, including potential overfitting issues and lack of interpretability. Here, we propose ResoNet, a DL model that combines convolutional neural network (CNN) and Transformer architectures. This hybrid architecture design enables our model to adequately capture local SSTA as well as long-range inter-basin interactions across oceans. We show that ResoNet can robustly predict ESNO at lead times between 19 and 26 months, thus outperforming existing approaches in terms of the forecast horizon. According to an explainability method applied to ResoNet predictions of El Ni\~no and La Ni\~na events from 1- to 18-month lead, we find that it predicts the Ni\~no3.4 index based on multiple physically reasonable mechanisms, such as the Recharge Oscillator concept, Seasonal Footprint Mechanism, and Indian Ocean capacitor effect. Moreover, we demonstrate that for the first time, the asymmetry between El Ni\~no and La Ni\~na development can be captured by ResoNet. Our results could help alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.

Our society relies heavily on plastic products and the amount of plastic waste is expected to increase in the future. If not properly discarded or recycled, much of it accumulates in rivers and lakes. Eventually it will flow into the oceans, where it can form aggregations of marine debris together with natural materials like driftwood and algae. A new study from Wageningen University and EPFL researchers, recently published in Cell iScience, has developed an artificial intelligence-based detector that estimates the probability of marine debris shown in satellite images. This could help to systematically remove plastic litter from the oceans with ships.

Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of applications across various sectors of society. Recently, state-of-the-art machine learning based weather forecasting models have made significant advancements, outperforming the high-resolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts (ECMWF). However, the full potential of machine learning models in subseasonal forecasts has yet to be fully explored. In this study, we introduce FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning based subseasonal forecasting model that provides global daily mean forecasts up to 42 days, covering 5 upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S integrates an enhanced FuXi base model with a perturbation module for flow-dependent perturbations in hidden features, and incorporates Perlin noise to perturb initial conditions. The model is developed using 72 years of daily statistics from ECMWF ERA5 reanalysis data. When compared to the ECMWF Subseasonal-to-Seasonal (S2S) reforecasts, the FuXi-S2S forecasts demonstrate superior deterministic and ensemble forecasts for total precipitation (TP), outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Although it shows slightly inferior performance in predicting 2-meter temperature (T2M), it has clear advantages over land area. Regarding the extreme forecasts, FuXi-S2S outperforms ECMWF S2S globally for TP. Furthermore, FuXi-S2S forecasts surpass the ECMWF S2S reforecasts in predicting the Madden Julian Oscillation (MJO), a key source of subseasonal predictability. They extend the skillful prediction of MJO from 30 days to 36 days.

Recent research has demonstrated that the multi-task fine-tuning of multi-modal Large Language Models (LLMs) using an assortment of annotated downstream vision-language datasets significantly enhances their performance. Yet, during this process, a side effect, which we termed as the "multi-modal alignment tax", surfaces. This side effect negatively impacts the model's ability to format responses appropriately -- for instance, its "politeness" -- due to the overly succinct and unformatted nature of raw annotations, resulting in reduced human preference. In this paper, we introduce Polite Flamingo, a multi-modal response rewriter that transforms raw annotations into a more appealing, "polite" format. Polite Flamingo is trained to reconstruct high-quality responses from their automatically distorted counterparts and is subsequently applied to a vast array of vision-language datasets for response rewriting. After rigorous filtering, we generate the PF-1M dataset and further validate its value by fine-tuning a multi-modal LLM with it. Combined with novel methodologies including U-shaped multi-stage tuning and multi-turn augmentation, the resulting model, Clever Flamingo, demonstrates its advantages in both multi-modal understanding and response politeness according to automated and human evaluations.

Fox News chief national security correspondent Jennifer Griffin reports on the repeated attacks on U.S. forces in the Middle East on'Faulkner Focus.' U.S. Navy destroyer USS Mason shot down a Houthi drone coming out of Yemen on Wednesday, a U.S. defense official told Fox News. The drone was headed toward USS Mason, which was responding to reports that Houthis were attacking the tanker Ardmore Encounter by using skiffs and then firing two missiles that missed, according to the official. No damage or injuries were initially reported, and the Ardmore Encounter went on its way. The incident occurred around 8 a.m. A Pentagon official confirmed to Fox News that the two missiles were anti-ship ballistic missiles fired from ground-based locations in Yemen.

This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information. Recognizing the inherent challenges in extending the context window for LLMs, primarily built on Transformer architecture, we propose a new model architecture, referred to as Zebra. This architecture efficiently manages the quadratic time and memory complexity issues associated with full attention in the Transformer by employing grouped local-global attention layers. Our model, akin to a zebra's alternating stripes, balances local and global attention layers, significantly reducing computational requirements and memory consumption. Comprehensive experiments, including pretraining from scratch, continuation of long context adaptation training, and long instruction tuning, are conducted to evaluate the Zebra's performance. The results show that Zebra achieves comparable or superior performance on both short and long sequence benchmarks, while also enhancing training and inference efficiency.

Yemen's Houthi movement said on Tuesday they struck a Norwegian oil and chemical tanker with a rocket in its latest operation to protest against Israel's bombardment of Gaza. The Iran-aligned group targeted the ship after its crew "rejected all warning calls," Houthi military spokesperson Yehia Sareea said in a televised statement. He vowed that the Houthis would continue blocking ships heading to Israeli ports until Israel allows the entry of food and medical aid into the Gaza Strip - more than 1,000 miles from the Houthi seat of power in Sanaa. NETANYAHU TELLS BIDEN ISRAEL WILL ACT MILITARILY AGAINST YEMEN'S HOUTHIS IF US WON'T: REPORT The attack on the tanker Strinda took place about 60 nautical miles north of the Bab al-Mandab Strait. The ship, a Norwegian-owned-and-operated vessel called Strinda, was struck on Monday night as it traveled near the Bab al-Mandab strait, a sea lane through which much of the world's oil is shipped.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. American technology is scoring big against Iran-backed threats in the Red Sea region, and it's bad news for China. You know the U.S. Navy destroyer USS Carney was in the news again Sunday, shooting down drones launched from Yemen's Houthi rebels against merchant shipping in the Red Sea. This crew has been taking out drones and missiles supplied by Iran for weeks now, and their tally is over two dozen destroyed so far.

A French frigate has shot down two drones over the Red Sea that were believed to be approaching from the coast of Yemen, according to the French military. "The interception and destruction of these two identified threats" were carried out late on Saturday by the frigate Languedoc, which operates in the Red Sea, the general staff said in a press release on Sunday. The interceptions happened at 20:30 GMT and 22:30 GMT and were 110km (68 miles) from the Yemeni coast, it added. Yemen's Iran-backed Houthi rebels on Saturday threatened to attack any vessels heading to Israeli ports unless food and medicine were allowed into the besieged Gaza Strip, which has been devastated by more than two months of Israeli bombing. The Houthis said that all "ships linked to Israel or that will transport goods to Israeli ports" are not welcome in the Red Sea, a vital channel for global trade linked to the Suez Canal.