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.

Rear Admiral Daniel Hagari of the Israeli Defense Forces updates the public Saturday, April 13 on Iranian UAVs being launched toward the Jewish state. Iran launched dozens of unmanned aerial vehicles (UAV), also known as drones, at Israel on Saturday evening local time after a week of threatening retaliation for an attack on a consulate in Damascus. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." Iran's state-run news agency Mehr News reported that the Islamic Revolutionary Guard Corps (IRGC) had announced the start of the "anti-Zionist operation," which would hit targets in the Palestinian territories, with further details of the operation announced soon.

Rep. Carlos Gimenez, R-Fla., tells'Fox News Live' that China, Russia, North Korea and Iran want to establish a'new world order.' Lawmakers reacted after Iran launched drones from its own territory toward Israel late Saturday, calling for the White House to "stand firm" and "stop coddling Iran." Speaker Mike Johnson pledged America's "full resolve" to stand with Israel. "As Israel faces this vicious attack from Iran, America must show our full resolve to stand with our critical ally," Johnson said in a statement. "The world must be assured: Israel is not alone."

The White House vowed Saturday that the United States' support for Israel's security is "ironclad," pledging to stand with the Jewish state and "support their defense" after Iran launched an aerial drone attack towards the country Saturday afternoon. Iran launched drones from its own territory toward Israel late Saturday, days after its Supreme Leader warned it would hit back in response to an airstrike on the Iranian consulate in Syria that left several generals dead. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." The White House said the president's team "is in constant communication with Israeli officials as well as other partners and allies."

We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours. In particular, we present a vision-language model which employs multi-modal decoding of visual features extracted directly from camera trap videos to process query tokens representing behaviours and output class predictions. Query tokens are initialised using a standardised ethogram of chimpanzee behaviour, rather than using random or name-based initialisations. In addition, the effect of initialising query tokens using a masked language model fine-tuned on a text corpus of known behavioural patterns is explored. We evaluate our system on the PanAf500 and PanAf20K datasets and demonstrate the performance benefits of our multi-modal decoding approach and query initialisation strategy on multi-class and multi-label recognition tasks, respectively. Results and ablations corroborate performance improvements. We achieve state-of-the-art performance over vision and vision-language models in top-1 accuracy (+6.34%) on PanAf500 and overall (+1.1%) and tail-class (+2.26%) mean average precision on PanAf20K. We share complete source code and network weights for full reproducibility of results and easy utilisation.