Iranian-backed Houthis rebels have claimed responsibility for a U.S. MQ-9 Reaper drone crash off the coast of Yemen on Thursday, Fox News confirmed on Friday. Thursday's crash is the fourth remotely piloted drone brought down by Iranian-proxy groups since November, costing the U.S. government upwards of 120 million. It is also the third time Houthi rebels have brought down a U.S. MQ-9 drone. Remotely piloted MQ-9 Reaper drones cost around 30 million each. Last fall, the Houthis released video of a reaper drone the rebels shot down on Nov. 8, one day after Hamas' unprovoked attack on Israel.

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources.This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format. As a result, we present TIGQA, an expert annotated educational dataset consisting of 2.68K question-answer pairs covering 122 diverse topics such as climate, water, and traffic. These pairs are from 537 context paragraphs in publicly accessible Tigrinya and Biology books. Through comprehensive analyses, we demonstrate that the TIGQA dataset requires skills beyond simple word matching, requiring both single-sentence and multiple-sentence inference abilities. We conduct experiments using state-of-the art MRC methods, marking the first exploration of such models on TIGQA. Additionally, we estimate human performance on the dataset and juxtapose it with the results obtained from pretrained models.The notable disparities between human performance and best model performance underscore the potential for further enhancements to TIGQA through continued research. Our dataset is freely accessible via the provided link to encourage the research community to address the challenges in the Tigrinya MRC.

Tesla reported a 55 percent drop in profit amid fierce competition in the electric vehicle market, but shares rallied on plans to accelerate the production of more affordable models. The Austin, Texas-based company on Tuesday reported profits of 1.1bn in the first quarter, down from 2.51bn a year ago. But shares of Tesla soared by 11 percent after CEO Elon Musk said that production of new, more affordable vehicles would begin in the second half of next year "if not late this year". The models "will use new aspects of the next generation platform as well as aspects of our current platform", Musk said on a conference call with analysts. Musk did not elaborate on the new vehicles, saying more details would be released in August.

With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, visual reasoning problem is usually decomposed into manageable sub-tasks and tackled sequentially with various external tools. However, such a paradigm faces the challenge of the potential "determining hallucinations" in decision-making due to insufficient visual information and the limitation of low-level perception tools that fail to provide abstract summaries necessary for comprehensive reasoning. We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. This paper delves into the realm of multimodal CoT to solve intricate visual reasoning tasks with multimodal large language models(MLLMs) and their cognitive capability. To this end, we propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Cantor first acts as a decision generator and integrates visual inputs to analyze the image and problem, ensuring a closer alignment with the actual context. Furthermore, Cantor leverages the advanced cognitive functions of MLLMs to perform as multifaceted experts for deriving higher-level information, enhancing the CoT generation process. Our extensive experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance across two complex visual reasoning datasets, without necessitating fine-tuning or ground-truth rationales. Project Page: https://ggg0919.github.io/cantor/ .

We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees, and for analyzing and quantifying the effectiveness of biodiversity conservation measures in the Yarlung Tsangpo Grand Canyon (YTGC) National Nature Reserve. We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling. And we customized a pyramid receptive fields depth separable CNN (PRFXception). PRFXception, a CNN architecture specifically customized for mapping primeval forest canopy height to infer the canopy height at the footprint level of GEDI and ICESat-2 from Sentinel-2 optical imagery with a 10-meter spatial resolution. We conducted a field survey of 227 permanent plots using a stratified sampling method and measured several giant trees using UAV-LS. The predicted canopy height was compared with ICESat-2 and GEDI validation data (RMSE =7.56 m, MAE=6.07 m, ME=-0.98 m, R^2=0.58 m), UAV-LS point clouds (RMSE =5.75 m, MAE =3.72 m, ME = 0.82 m, R^2= 0.65 m), and ground survey data (RMSE = 6.75 m, MAE = 5.56 m, ME= 2.14 m, R^2=0.60 m). We mapped the potential distribution map of world-level giant trees and discovered two previously undetected giant tree communities with an 89% probability of having trees 80-100 m tall, potentially taller than Asia's tallest tree. This paper provides scientific evidence confirming southeastern Tibet--northwestern Yunnan as the fourth global distribution center of world-level giant trees initiatives and promoting the inclusion of the YTGC giant tree distribution area within the scope of China's national park conservation.

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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.

Corals often provide a colorful backdrop to photographs of shimmering fish captured by amateur divers. Corals – marine invertebrates with calcium-carbonate exoskeletons – are some of the most diverse ecosystems on Earth: despite covering less than 0.1% of the ocean's surface, they provide shelter and habitats for almost one-third of known marine species. Their impact also extends to human populations in many countries around the world. According to research by the U.S. National Oceanic and Atmospheric Administration, up to half a billion people worldwide rely on coral reefs for food security and tourist income. But the world's corals are under threat from rising sea temperatures and local anthropogenic pollution, which causes them to bleach and die.

Despite their strong ability to retrieve knowledge in English, current large language models show imbalance abilities in different languages. Two approaches are proposed to address this, i.e., multilingual pretraining and multilingual instruction tuning. However, whether and how do such methods contribute to the cross-lingual knowledge alignment inside the models is unknown. In this paper, we propose CLiKA, a systematic framework to assess the cross-lingual knowledge alignment of LLMs in the Performance, Consistency and Conductivity levels, and explored the effect of multilingual pretraining and instruction tuning on the degree of alignment. Results show that: while both multilingual pretraining and instruction tuning are beneficial for cross-lingual knowledge alignment, the training strategy needs to be carefully designed. Namely, continued pretraining improves the alignment of the target language at the cost of other languages, while mixed pretraining affect other languages less. Also, the overall cross-lingual knowledge alignment, especially in the conductivity level, is unsatisfactory for all tested LLMs, and neither multilingual pretraining nor instruction tuning can substantially improve the cross-lingual knowledge conductivity.

Fox News chief national security correspondent Jennifer Griffin reports on how autonomous weapons used in Ukraine have transformed the battlefield on'Special Report.' A series of scandals has blighted Denmark's Armed Forces at a time when the Scandinavian country and member of the NATO alliance is building up its defenses, chiefly as a response to Russia's invasion of Ukraine. The events have so far led to the dismissal this week of Denmark's top military chief, Gen. Flemming Lentfer, who failed to inform the defense minister about an incident on the frigate HDMS Iver Huitfeldt last month while deployed to the Red Sea, where it was part of a U.S.-led operation to defend commercial shipping against Houthi militants. On Thursday, a technical error onboard its sister ship, the frigate HDMS Niels Juel that was docked in a Danish harbor, led to the air space and maritime route being briefly closed due to fears a navy missile might launch unintentionally -- but not explode -- and send fragments falling into the busy shipping lane between the islands of Zeeland, where Copenhagen sits, and Funen. The Iver Huitfeldt, which returned from its Red Sea mission on Thursday, ahead of schedule, reportedly experienced a half-hour long malfunction of its missile and radar systems during a drone attack on March 9, according to the specialist defense news website Olfi.