We provide an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first provide a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key specific challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks ( learning to communicate ), and as an intrinsic in-network intelligence feature ( communicating to learn or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning (FL) and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Finally, a brief discussion of the ongoing regulation activities related to AI, is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G.

At the pandemic's beginning, significant concern has risen about the prevalence of myocardial involvement after SARS‐CoV‐2 infection. We assessed the cardiovascular burden of SARS‐CoV‐2 in a large cohort of athletes and identified factors that might affect the disease course. We included 633 athletes in our study on whom we performed extensive cardiology examinations after recovering from SARS‐CoV‐2 infection. More than half of the athletes (n = 322) returned for a follow‐up examination median of 107 days after the commencement of their infection. Troponin T positivity was as low as 1.4% of the athletes, where the subsequently performed examinations did not show definitive, ongoing myocardial injury. Altogether, 31% of the athletes' rapid training rebuild was hindered by persistent or reoccurring symptoms. Female athletes reported a higher prevalence of return to play (RTP) symptoms than their male counterparts (34% vs. 19%, p = 0.005). The development of long COVID symptoms was independently predicted by increasing age and acute symptoms' severity in a multiple regression model (AUC 0.75, CI 0.685–0.801). Athletes presenting with either or both cough and ferritin levels higher than >150 μg/L had a 4.1x (CI 1.78–9.6, p = 0.001) higher odds ratio of developing persistent symptoms. While SARS‐CoV‐2 rarely affects the myocardium in athletes, about one in three of them experience symptoms beyond the acute phase. Identifying those athletes with a predisposition to developing long‐standing symptoms may aid clinicians and trainers in finding the optimal return‐to‐play timing and training load rebuild pace.

Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics.