Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks

Meta is implementing changes to simplify Facebook, aiming to improve user experience by streamlining various features and functionalities. This initiative reflects the company's ongoing efforts to address user feedback and enhance engagement on the platform.

Google is set to launch its AI glasses next year, featuring audio and display options that promise screen-free assistance, live translations, and seamless smartphone integration. This move positions Google to compete directly with Meta's offerings in the augmented reality space.

A Border Patrol agent was recorded wearing Meta's Ray-Ban smart glasses during a raid, with the recording light clearly visible, despite a Department of Homeland Security (DHS) ban on officers using personal devices for recording. This incident raises concerns about compliance with regulations intended to protect privacy and civil liberties.

The emergence of 6G technology is set to revolutionize connectivity, promising ultra-fast and intelligent networks that will transform industries and enhance everyday life. Predictions suggest that this next-generation network will significantly improve global communication capabilities, paving the way for innovations across various sectors.

Meta has received approval from the European Union for its revised advertising experience, following penalties under the Digital Markets Act (DMA). This approval comes after the company faced scrutiny for its data practices and advertising models, which were deemed non-compliant with EU regulations.

A new dataset called EgoCampus has been introduced, focusing on egocentric pedestrian eye gaze during navigation in outdoor campus settings. This dataset includes recordings from over 80 pedestrians across 25 unique paths totaling 6 km, utilizing Meta's Project Aria glasses to capture eye tracking and environmental data.

A recent study has demonstrated the application of Post-Training Quantization (PTQ) to optimize Large Language Models (LLMs) for mobile execution, specifically focusing on Meta's Llama 3.2 3B model. The research achieved a 68.66% reduction in model size through 4-bit quantization, enabling efficient inference on Android devices using the Termux environment and the Ollama framework.

The study explores the trade-off between accuracy and computational efficiency in time series classification, highlighting the limitations of comprehensive ensembles like HIVE-COTE 2.0, which require extensive training time. By combining Hydra and Quant algorithms, the research evaluates performance across ten large-scale MONSTER datasets, achieving a mean accuracy improvement from 0.829 to 0.836 in seven datasets. However, the findings reveal a significant meta-learning optimization gap, with prediction-combination ensembles capturing only 11% of theoretical potential.