Satellite internet is blasting off right now. Nations and states are inking deals with satellite providers to fill in service gaps for their residents and keep their critical infrastructure connected. Phone makers are building satellite capabilities into their handsets. Airlines are partnering with satellite operators to keep your in-flight Netflix stream stutter-free. And the race to blast the satellites powering these networks into orbit is helping the rocket business thrive.

At Fort Sill, service members from across the military are undergoing counter-drone training at the Joint C-sUAS (Counter small Unmanned Aircraft System) University (JCU), also known as "drone university." The program has become a critical part of the Military's efforts to combat the rapidly growing use of unmanned aerial systems (UAS) by adversaries. "It's the Army's premier Counter-Small UAS training institution," said Col. Moseph Sauda, the program's director. "Our mission is to prepare and train the joint force to counter the threat, to be able to understand that threat, how they operate, and how they attack us… We can then develop not only tactics, techniques, and procedures, but also the employment methodology that maximizes the capabilities of our existing systems." A 3D-printed drone flies above from Oklahoma's Fort Sill at the U.S. Army's Joint C-sUAS University.

The'America's Got Talent' judge told Fox News Digital why he doesn't like AI technology in songwriting. "We firmly believe that America's global AI leadership must not come at the expense of our essential creative industries," the letter, addressed to Trump's Office of Science and Technology Policy and shared by Deadline and Variety, began. "America's arts and entertainment industry supports over 2.3M American jobs with over 229Bn in wages annually, while providing the foundation for American democratic influence and soft power abroad. The letter was submitted as part of comments on the Trump administration's U.S. AI Action Plan. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? SIMON COWELL WARNS AI'SHOULDN'T BE ABLE TO STEAL' HUMAN TALENT "Access to America's creative catalog of films, writing, video content, and music is not a matter of national security.

Regional authorities in northeast Ukraine's Sumy region said Russian drone attacks damaged two hospitals there, while a 29-year-old man was killed and three others were injured in a separate attack on a residential building. Kyiv region's Governor Mykola Kalashnyk said Kremlin drones damaged several houses near the Ukrainian capital, resulting in a 60-year-old man being injured. Ukraine's state railway network Ukrzaliznytsia said Moscow's forces attacked its power system twice in the city of Dnipro, with the first strike hitting just hours after Russian President Vladimir Putin committed to a 30-day pause on attacks on Ukraine's energy infrastructure. The second attack injured four people. "Russia is attacking civilian infrastructure and people – right now," said Andriy Yermak, Ukrainian President Volodymyr Zelenskyy's chief of staff.

Physics-informed machine learning provides an approach to combining data and governing physics laws for solving complex partial differential equations (PDEs). However, efficiently solving PDEs with varying parameters and changing initial conditions and boundary conditions (ICBCs) with theoretical guarantees remains an open challenge. We propose a hybrid framework that uses a neural network to learn B-spline control points to approximate solutions to PDEs with varying system and ICBC parameters. The proposed network can be trained efficiently as one can directly specify ICBCs without imposing losses, calculate physics-informed loss functions through analytical formulas, and requires only learning the weights of B-spline functions as opposed to both weights and basis as in traditional neural operator learning methods. We provide theoretical guarantees that the proposed B-spline networks serve as universal approximators for the set of solutions of PDEs with varying ICBCs under mild conditions and establish bounds on the generalization errors in physics-informed learning. We also demonstrate in experiments that the proposed B-spline network can solve problems with discontinuous ICBCs and outperforms existing methods, and is able to learn solutions of 3D dynamics with diverse initial conditions.

The future work section of a scientific article outlines potential research directions by identifying gaps and limitations of a current study. This section serves as a valuable resource for early-career researchers seeking unexplored areas and experienced researchers looking for new projects or collaborations. In this study, we generate future work suggestions from key sections of a scientific article alongside related papers and analyze how the trends have evolved. We experimented with various Large Language Models (LLMs) and integrated Retrieval-Augmented Generation (RAG) to enhance the generation process. We incorporate a LLM feedback mechanism to improve the quality of the generated content and propose an LLM-as-a-judge approach for evaluation. Our results demonstrated that the RAG-based approach with LLM feedback outperforms other methods evaluated through qualitative and quantitative metrics. Moreover, we conduct a human evaluation to assess the LLM as an extractor and judge. The code and dataset for this project are here, code: HuggingFace

Abstract--3D intelligence leverages rich 3D features and stands as a promising frontier in AI, with 3D rendering fundamental to many downstream applications. Previous efforts to accelerate 3DGS rely on dedicated accelerators that require substantial integration overhead and hardware costs. These platforms are increasingly crucial due to AI by leveraging rich 3D features to enhance understanding the growing demand for 3D processing in mobile and embedded and interaction within complex environments. Specifically, 3DGS achieves only Fei Li, co-founder of ImageNet, emphasized, "...we need 2-5 FPS on these platforms [22] with commonly used realworld, spatially intelligent AI that can model the world and reason large-scale datasets [3], falling short of the performance about objects, places, and interactions in 3D space and requirement for most practical applications. This underscores the importance of 3D intelligent gap poses challenges for deploying advanced 3D intelligence applications such as autonomous driving [39], robotics [32], in resource-constrained environments, highlighting the need and augmented/virtual reality (AR/VR) [4] shown in Figure 1.

Identifying claims requiring verification is a critical task in automated fact-checking, especially given the proliferation of misinformation on social media platforms. Despite significant progress in the task, there remain open challenges such as dealing with multilingual and multimodal data prevalent in online discourse. Addressing the multilingual challenge, recent efforts have focused on fine-tuning pre-trained multilingual language models. While these models can handle multiple languages, their ability to effectively transfer cross-lingual knowledge for detecting claims spreading on social media remains under-explored. In this paper, we introduce EX-Claim, an entity-aware cross-lingual claim detection model that generalizes well to handle claims written in any language. The model leverages entity information derived from named entity recognition and entity linking techniques to improve the language-level performance of both seen and unseen languages during training. Extensive experiments conducted on three datasets from different social media platforms demonstrate that our proposed model significantly outperforms the baselines, across 27 languages, and achieves the highest rate of knowledge transfer, even with limited training data.

While detecting and avoiding bias in LLM-generated text is becoming increasingly important, media bias often remains subtle and subjective, making it particularly difficult to identify and mitigate. In this study, we assess media bias in LLM-generated content and LLMs' ability to detect subtle ideological bias. We conduct this evaluation using two datasets, PoliGen and EconoLex, covering political and economic discourse, respectively. We evaluate eight widely used LLMs by prompting them to generate articles and analyze their ideological preferences via self-assessment. By using self-assessment, the study aims to directly measure the models' biases rather than relying on external interpretations, thereby minimizing subjective judgments about media bias. Our results reveal a consistent preference of Democratic over Republican positions across all models. Conversely, in economic topics, biases vary among Western LLMs, while those developed in China lean more strongly toward socialism.

In particular, the ChatGPT model (GPT-3.5 and GPT-4) [1] has demonstrated its potential to generate human-like conversational abilities, enabling it to engage in meaningful dialogues, answer questions, and generate text across a wide range of topics, including science, entertainment, and politics [13, 14, 20]. The ability of these models to generate coherent and contextually relevant text has made them a powerful tool for content creation and enabling new ways of human-machine interactions. Despite their potential benefits, the widespread adoption of LLMs has raised concerns about their potential misuse, particularly in generating disinformation [16, 23, 25], fake news [11, 27], and hate speech [10, 22]. Beyond these widely recognized concerns, another critical issue has gained increasing attention in recent months: the potential of these models to manipulate public opinion, both due to the inherent biases embedded in their training process and the biases deliberately introduced or reinforced by their developers or maintainers. The most modern LLMs designed to interact with humans are generally trained using at least two phases. First, they are trained on large-scale text corpora, which inevitably incorporate the ideological, cultural, and political perspectives present in the source.