The Palestinian Hamas group has expressed support for Iran after it launched hundreds of missiles and drones at Israel in retaliation for a deadly assault on its consulate in the Syrian capital, Damascus. In a statement on Saturday, Hamas, which governs the Palestinian enclave of Gaza, said it affirmed "the natural right" of countries and peoples in the Middle East to defend themselves "in the face of Zionist aggressions". "The military operation carried out by Iran against the Zionist entity is a natural right and a due response to the crime of targeting the consulate in Damascus," it said. The Iranian salvo, fired late on Saturday night, consisted of more than 300 cruise missiles, ballistic missiles and drones, according to the Israeli military. Some 99 percent of the projectiles were intercepted, an Israeli spokesman said, with help from France, the United Kingdom and the United States.

Fox News chief national security correspondent Jennifer Griffin reports that the U.S. military has shot down'dozens' of ballistic and cruise missiles as well as attack drones. International world leaders collectively condemned Iran's drone and missile attacks on Israel, calling for peace in the Middle East. British Prime Minister Rishi Sunak said he condemned Iran's "reckless" attack against Israel. "I condemn in the strongest terms the Iranian regime's reckless attack against Israel," Sunak said in a statement. "Iran has once again demonstrated that it is intent on sowing chaos in its own backyard."

Fox News chief national security correspondent Jennifer Griffin reports that the U.S. military has shot down'dozens' of ballistic and cruise missiles as well as attack drones. Israelis breathed easier in the early hours of Sunday, after an attack from Iranian drones and missiles largely fizzled in the face of the Jewish state's powerful air defenses with assistance from the U.S. military and other allies. A senior US official told Fox News that the U.S. military has shot down "at least dozens" of Iranian drones en route to Israel early Sunday local time, adding that U.S. military engagement is ongoing. "U.S. forces in the region continue to shoot down Iranian-launched drones targeting Israel. Our forces remain postured to provide additional defensive support and to protect U.S. forces operating in the region," a U.S. military official told Fox News.

Trump's third visit to the battleground state comes just one day after his election integrity joint press conference with House Speaker Mike Johnson. Following Iran's attack on Israel, former President Trump shared a heated message to those in attendance at his rally in Pennsylvania on Saturday, vowing it should not have happened. "Before going any further, I want to say God bless the people of Israel. That's because we show great weakness," Trump said to open his speech. "The weakness that we've shown is unbelievable, and it would not have happened if we were in office. They know that, and everybody knows that."

On Saturday, Iran launched more than 200 drones and cruise missiles at Israel. As the drones made their way across the Middle East en route to their target, Israel has invoked a number of defense systems to impede their progress. None will be more important than the Iron Dome. The Iron Dome, operational for well over a decade, comprises at least 10 missile-defense batteries strategically distributed around the country. When radar detects incoming objects, it sends that information back to a command-and-control center, which will track the threat to assess whether it's a false alarm, and where it might hit if it's not.

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our study leverages data from 19 languages, including ten indigenous or endangered languages across 10 language families with diverse morphological systems (polysynthetic, fusional, and agglutinative) and different degrees of data availability. We conduct large-scale experimentation with varying sized combinations of training and evaluation sets as well as new test data. Our results show that, when faced with new test data: (1) models trained from random splits are able to achieve higher numerical scores; (2) model rankings derived from random splits tend to generalize more consistently.

Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.

Remote sensing semantic segmentation (RSS) is an essential task in Earth Observation missions. Due to data privacy concerns, high-quality remote sensing images with annotations cannot be well shared among institutions, making it difficult to fully utilize RSS data to train a generalized model. Federated Learning (FL), a privacy-preserving collaborative learning technology, is a potential solution. However, the current research on how to effectively apply FL in RSS is still scarce and requires further investigation. Remote sensing images in various institutions often exhibit strong geographical heterogeneity. More specifically, it is reflected in terms of class-distribution heterogeneity and object-appearance heterogeneity. Unfortunately, most existing FL studies show inadequate focus on geographical heterogeneity, thus leading to performance degradation in the global model. Considering the aforementioned issues, we propose a novel Geographic Heterogeneity-Aware Federated Learning (GeoFed) framework to address privacy-preserving RSS. Through Global Feature Extension and Tail Regeneration modules, class-distribution heterogeneity is alleviated. Additionally, we design an Essential Feature Mining strategy to alleviate object-appearance heterogeneity by constructing essential features. Extensive experiments on three datasets (i.e., FBP, CASID, Inria) show that our GeoFed consistently outperforms the current state-of-the-art methods. The code will be available publicly.

Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those who have investigated dust aerosols using sensors onboard of satellites using machine learning-based approaches. We have reviewed the most common issues revolving dust aerosol modeling using different datasets and different sensors from a historical perspective. Our findings suggest that multi-spectral approaches based on linear and non-linear combinations of spectral bands are some of the most successful for visualization and quantitative analysis; however, when researchers have leveraged machine learning, performance has been improved and new opportunities to solve unique problems arise.

Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation, we introduce a novel method that maps factor tensors onto a unified smooth Lie group manifold to make the distribution of factor tensors approximating homogeneous in tensor decomposition. We provide the theoretical proof of our motivation that homogeneous tensors are more effective than heterogeneous tensors in tensor fusion and approximating the target for tensor decomposition based TKGE methods. The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. Extensive experiments demonstrate the effectiveness of our method in mitigating the heterogeneity and in enhancing the tensor decomposition based TKGE models.