We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on today's Internet. Processors in our model do not have an internal state, but rather interact by repeatedly mapping incoming messages ("labels") to outgoing messages and output values. While seemingly too restrictive to be of interest, stateless computation encompasses both classical game-theoretic notions of strategic interaction and a broad range of practical applications (e.g., Internet protocols, circuits, diffusion of technologies in social networks). Our main technical contribution is a general impossibility result for stateless self-stabilization in our model, showing that even modest asynchrony (with wait times that are linear in the number of processors) can prevent a stateless protocol from reaching a stable global configuration. Furthermore, we present hardness results for verifying stateless self-stabilization. We also address several aspects of the computational power of stateless protocols. Most significantly, we show that short messages (of length that is logarithmic in the number of processors) yield substantial computational power, even on very poorly connected topologies.

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively.

In W3C's Rule Interchange Format (RIF), F-Logic rules have received considerable attention as a major logical rule formalism, while combinations of rules with Description Logic (DL) ontologies in RIF, let alone with F-Logic rules, are far less developed. To mend this, we first present F-Logic# knowledge bases, a framework based on the semantics of the well-investigated dl-programs, that provides a loose coupling approach to integrating F-Logic rules and DL ontologies by allowing rules to query the ontology using external atoms. We investigate the semantical properties of this framework and define a stratified fragment that allows for fast reasoning - a necessity on a Web with large amounts of data. We then shape F-Logic# as a RIF dialect, setting it firmly in a Web context and providing an expressive combination of F-Logic rules with DL ontologies in RIF. Finally, we show how to extend the F-Logic rule engine OntoBroker towards reasoning with F-Logic#, enabling as such a first commercial implementation for loosely-coupled ontologies and rules.

In this paper we present an approach towards knowledgeacquisition of process knowledge for the natural sciences.The work has been conducted within Project Halo, whichis creating advanced knowledge authoring and questionanswering systems for the natural sciences. An analysis of AP®-level questions for Biology, Chemistry and Physicsuncovered that process knowledge is the single most frequenttype of knowledge required. Thus, we developedmeans to acquire process knowledge, to formally representit, and to reason about it in order to answer novel questionsabout the domains.All these tasks are supported by an abstract process metamodel.It provides the terminology for user-tailored processdiagrams, which are automatically translated into executableFLogic code. The meta-model and the code generationare based on the notion of Problem Solving Methods(PSM) which represent an abstract formalization of thereasoning strategies needed for processes.

Ontobroker applies Artificial Intelligence techniques to improve access to heterogeneous, distributed and semi-structured information sources as they are presented in the World Wide Web or organization-wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries and derive answers. In this paper we will briefly sketch Ontobroker. Then we will discuss its main shortcomings, i.e. we will share the lessons we learned from our exercise. We will also show how On2broker overcomes these limitations. Most important is the separation of the query and inference engines and the integration of new web standards like XML and RDF.

It is argued that randomization is a useful primitive for the solution of robot tasks under uncertainty. The author demonstrates a possible application using a standard peg-in-hole problem, focusing on randomization within simple feedback strategies. More generally, randomization may be thought of as an operator that randomly selects between possible knowledge states of actions. In order to synthesize randomized strategies, it is possible to use this operator within the dynamic programming methodology essentially as one would any other operator. It is further asserted that randomization has three important properties: (1) increases the class of solvable tasks; (2) reduces plan brittleness; and (3) simplifies the planning process.>