Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies and process innovations promises to enhance connected health by integrating various healthcare data sources. This integration aims to personalize care, predict health outcomes, and streamline patient management, though challeng-es remain, particularly in data architecture, application interoperability, and security. Data analytics can provide critical insights for informed decision-making and health co-creation, but solutions must prioritize end-users, including patients and healthcare professionals. This perspective was explored through an agile System Development Lifecycle in an EU-funded project aimed at developing an integrated AI-generated solution for managing cancer patients undergoing immunotherapy. This paper contributes with a collaborative digital framework integrating stakeholders across the care continuum, leveraging federated big data analytics and artificial intelligence for improved decision-making while ensuring privacy. Analytical capabilities, such as treatment recommendations and adverse event predictions, were validated using real-life data, achieving 70%-90% accuracy in a pilot study with the medical partners, demonstrating the framework's effectiveness.

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.

This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that captures the full structure of biased patterns. Fair representations are then obtained via Delbaen Majumdar optimal transport, which generates variables independent of G while minimizing L2 distance to preserve accuracy. This guarantees true independence rather than mere decorrelation. By modeling bias as dependence between sigma algebras, compiling ontological knowledge into measurable structures, and using optimal transport as the unique fair transformation, the approach ensures complete fairness in tasks like loan approval, where proxies such as ZIP code reveal race. The result is a certifiable and mathematically grounded method for trustworthy AI.

While previous research has implicitly leveraged these hierarchies to enhance LMs, approaches for their explicit encoding are yet to be explored.

We investigated the effectiveness of Digit Embedding. We can see that the numbers in the (100090-100140) range are clustered together. Supplementary Materials for PERFOGRAPH: A Numerical A ware Program Graph Representation for Performance Optimization and Program Analysis We investigated with more ranges. Figure 3 shows the 2-d embedding of decimal numbers in the range [1.0, 10.0] and [20.0-31.0]. And the numbers with larger differences like (1.6478, 30.7010), (5.339, 30.5113) are far from Figure 3: Embedding of decimal numbers in the range [1.0, 10.0] and [20.0-31.0] 2 So, the above examples clearly demonstrate the effectiveness of Digit Embedding for generating the Please note that in this setup, the Digit Embedding is still applied.

Metadata is stored in a hidden directory in each project and users are not expected to manipulate it directly. This allows us to easily import datasets from external repositories (e.g. Metadata is added to Renku projects every time any of the entities in a project are created or updated. The metadata from different entities (e.g. a dataset file can also be an input to a An illustration of this structure is shown in Figure 1, which shows an example pipeline from raw data collection to model training. The Renku metadata in the KG can be extended with custom metadata using plugins for the CLI. CLI and executed alongside Renku functionality.