In this appendix, we include all the material missing from the main paper. Moreover, we restate a key result which connects random sampling and submodular maximization. The original version of the theorem was due to Feige et al. In fact, in what follows we exclusively use S and O for their final versions. Before stating the next lemma, let us introduce some notation for the sake of readability.

This is the focus of work by and, which won the best paper award at the recent RoboCup symposium . The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition. Could you start by giving us a brief description of the problem that you were trying to solve in your paper "Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots"? The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online.

In this appendix, we include all the material missing from the main paper. Moreover, we restate a key result which connects random sampling and submodular maximization. The original version of the theorem was due to Feige et al. In fact, in what follows we exclusively use S and O for their final versions. Before stating the next lemma, let us introduce some notation for the sake of readability.

We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~ 22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.

Rubedo is a dual-purpose game that allows investigating text prediction capabilities while entertaining the player. The human player reads a short story while gradually trying to guess the characters that compose it at the writing level. In the meanwhile, an AI system plays the same game, and as it keeps guessing the letters, it unveils the complete text. Who will be the winner between humans and machines? And most of all, is it possible to compare the strategies of human beings with those implemented by artificially intelligent systems?

Organizing committee for the  Cognitive Robotics Workshop held at AAAI-12.