Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sources. Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds graph structure to explicitly model these relationships, enabling more effective and efficient retrievers. Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure. To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. The GFM with 8M parameters undergoes a two-stage training process on large-scale datasets, comprising 60 knowledge graphs with over 14M triples and 700k documents. This results in impressive performance and generalizability for GFM-RAG, making it the first graph foundation model applicable to unseen datasets for retrieval without any fine-tuning required. Extensive experiments on three multi-hop QA datasets and seven domain-specific RAG datasets demonstrate that GFM-RAG achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws, highlighting its potential for further improvement.

Cyber threat detection has become an important area of focus in today's digital age due to the growing spread of fake information and harmful content on social media platforms such as Twitter (now 'X'). These cyber threats, often disguised within tweets, pose significant risks to individuals, communities, and even nations, emphasizing the need for effective detection systems. While previous research has explored tweet-based threats, much of the work is limited to specific languages, domains, or locations, or relies on single-model approaches, reducing their applicability to diverse real-world scenarios. To address these gaps, our study focuses on multi-lingual tweet cyber threat detection using a variety of advanced models. The research was conducted in three stages: (1) We collected and labeled tweet datasets in four languages English, Chinese, Russian, and Arabic employing both manual and polarity-based labeling methods to ensure high-quality annotations. (2) Each dataset was analyzed individually using machine learning (ML) and deep learning (DL) models to assess their performance on distinct languages. (3) Finally, we combined all four datasets into a single multi-lingual dataset and applied DL and large language model (LLM) architectures to evaluate their efficacy in identifying cyber threats across various languages. Our results show that among machine learning models, Random Forest (RF) attained the highest performance; however, the Bi-LSTM architecture consistently surpassed other DL and LLM architectures across all datasets. These findings underline the effectiveness of Bi-LSTM in multilingual cyber threat detection. The code for this paper can be found at this link: https://github.com/Mmurrad/Tweet-Data-Classification.git.

A man who was piloting a drone that collided with a firefighting aircraft working on the Palisades fire has agreed to plead guilty to a misdemeanor, pay a fine and complete community service, federal prosecutors said Friday. Peter Tripp Akemann, 56, of Culver City was charged with unsafe operation of an unmanned aircraft. He could still face up to a year in federal prison, prosecutors said. The drone, which authorities say was flying in restricted airspace on Jan. 9, put a fist-sized hole in the left wing of a Super Scooper -- a massive fixed-wing plane that can drop large amounts of water onto a fire. The collision knocked the plane out of commission for about five days and destroyed the drone.

Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.

Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling. While temporal-domain methods remain prevalent, frequency-domain approaches can effectively capture multi-scale periodic patterns, reduce sequence dependencies, and naturally denoise signals. However, existing approaches typically train model components for all frequencies under a unified training objective, often leading to mismatched learning speeds: high-frequency components converge faster and risk overfitting, while low-frequency components underfit due to insufficient training time. To deal with this challenge, we propose BEAT (Balanced frEquency Adaptive Tuning), a novel framework that dynamically monitors the training status for each frequency and adaptively adjusts their gradient updates. By recognizing convergence, overfitting, or underfitting for each frequency, BEAT dynamically reallocates learning priorities, moderating gradients for rapid learners and increasing those for slower ones, alleviating the tension between competing objectives across frequencies and synchronizing the overall learning process. Extensive experiments on seven real-world datasets demonstrate that BEAT consistently outperforms state-of-the-art approaches.

Traditional text-based human-AI interactions often adhere to a strict turn-taking approach. In this research, we propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Through a formative study, we observed that even in text-based contexts, users instinctively engage in overlapping behaviors like "A: Today I went to-" "B: yeah." To capitalize on these insights, we developed OverlapBot, a prototype chatbot where both AI and users can initiate overlapping. Our user study revealed that OverlapBot was perceived as more communicative and immersive than traditional turn-taking chatbot, fostering faster and more natural interactions. Our findings contribute to the understanding of design space for overlapping interactions. We also provide recommendations for implementing overlap-capable AI interactions to enhance the fluidity and engagement of text-based conversations.

In recent years, the application of machine learning approaches to time-series forecasting of climate dynamical phenomena has become increasingly active. It is known that applying a band-pass filter to a time-series data is a key to obtaining a high-quality data-driven model. Here, to obtain longer-term predictability of machine learning models, we introduce a new type of band-pass filter. It can be applied to realtime operational prediction workflows since it relies solely on past time series. We combine the filter with reservoir computing, which is a machine-learning technique that employs a data-driven dynamical system. As an application, we predict the multi-year dynamics of the El Ni\~no-Southern Oscillation with the prediction horizon of 24 months using only past time series.

This paper presents the UGSim, a simulator for buoyancy-driven gliders, with a LQR control strategy, and a recursive guidance system. Building on the top of the DAVE and the UUVsim, it is designed to address unique challenges that come from the complex hydrodynamic and hydrostatic impacts on buoyancy-driven gliders, which conventional robotics simulators can't deal with. Since distinguishing features of the class of vehicles, general controllers and guidance systems developed for underwater robotics are infeasible. The simulator is provided to accelerate the development and the evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea. It consists of a basic kinetic module, a LQR control module and a recursive guidance module, which allows the user to concentrate on the single problem rather than the whole robotics system and the software infrastructure. We demonstrate the usage of the simulator through an example, loading the configuration of the buoyancy-driven glider named Petrel-II, presenting its dynamics simulation, performances of the control strategy and the guidance system.

This article is part of The D.C. Brief, TIME's politics newsletter. Sign up here to get stories like this sent to your inbox. Stop me if you've heard this one: a tech tool owned by a foreign adversary is thrusting its tentacles into the devices in tens of millions of Americans' pockets, giving its owners the chance to harvest vast amounts of data about them while shaping how they interpret the world around them, either real or imagined. That was, in essence, why the U.S. Supreme Court just this month unanimously upheld a law effectively banning TikTok--because Congress saw it as a national security risk that stood to benefit China. Given the challenges coming from Beijing, justices said Washington was within its power to deny it one of its strongest toeholds out of concern that it could be used to surveil Americans, steal their secrets, and feed them a stream of propaganda useful to China's big-picture goals.

The launch of a new chatbot by Chinese artificial intelligence firm DeepSeek triggered a plunge in US tech stocks as it appeared to perform as well as OpenAI's ChatGPT and other AI models, but using fewer resources. By Monday, DeepSeek's AI assistant had rapidly overtaken ChatGPT as the most popular free app in Apple's US and UK app stores. Despite its popularity with international users, the app appears to censor answers to sensitive questions about China and its government. Chinese generative AI must not contain content that violates the country's "core socialist values", according to a technical document published by the national cybersecurity standards committee. That includes content that "incites to subvert state power and overthrow the socialist system", or "endangers national security and interests and damages the national image".