Inhibition of EMMPRIN/VEGFR-2 interaction decreases malignant phenotype of melanoma models

Trabajo presentado en el 15th European Association of Dermato-Oncology Congress (EADO), celebrado en París (Francia), del 24 al 27 de abril de 201

Theory combination: beyond equality sharing

Satisfiability is the problem of deciding whether a formula has a model. Although it is not even semidecidable in first-order logic, it is decidable in some first-order theories or fragments thereof (e.g., the quantifier-free fragment). Satisfiability modulo a theory is the problem of determining whether a quantifier-free formula admits a model that is a model of a given theory. If the formula mixes theories, the considered theory is their union, and combination of theories is the problem of combining decision procedures for the individual theories to get one for their union. A standard solution is the equality-sharing method by Nelson and Oppen, which requires the theories to be disjoint and stably infinite. This paper surveys selected approaches to the problem of reasoning in the union of disjoint theories, that aim at going beyond equality sharing, including: asymmetric extensions of equality sharing, where some theories are unrestricted, while others must satisfy stronger requirements than stable infiniteness; superposition-based decision procedures; and current work on conflict-driven satisfiability (CDSAT)

Semantically-guided goal-sensitive reasoning: inference system and completeness

We present a new method for clausal theorem proving, named SGGS from semantically-guided goal-sensitive reasoning. SGGS generalizes to first-order logic the Conflict-Driven Clause Learning (CDCL) procedure for propositional satisfiability. Starting from an initial interpretation,used for semantic guidance, SGGS employs a sequence of constrained clauses to represent a candidate model, instance generation to extend it, resolution and other inferences to explain and solve conflicts, amending the model. We prove that SGGS is refutationally complete and model complete in the limit, regardless of initial interpretation. SGGS is also goal sensitive, if the initial interpretation is properly chosen, and proof confluent, because it repairs the current model without undoing steps by backtracking. Thus, SGGS is a complete first-order methodthat is simultaneously model-based `a la CDCL, semantically-guided, goal-sensitive, and proof confluent