Algorithms for resolving majority cycles in preference aggregation have been studied extensively in computational social choice. Several sophisticated cycle-resolving methods, including Tideman's Ranked Pairs, Schulze's Beat Path, and Heitzig's River, are refinements of the Split Cycle (SC) method that resolves majority cycles by discarding the weakest majority victories in each cycle. Recently, Holliday and Pacuit proposed a new refinement of Split Cycle, dubbed Stable Voting, and a simplification thereof, called Simple Stable Voting (SSV). They conjectured that SSV is a refinement of SC whenever no two majority victories are of the same size. In this paper, we prove the conjecture up to 6 alternatives and refute it for more than 6 alternatives. While our proof of the conjecture for up to 5 alternatives uses traditional mathematical reasoning, our 6-alternative proof and 7-alternative counterexample were obtained with the use of SAT solving. The SAT encoding underlying this proof and counterexample is applicable far beyond SC and SSV: it can be used to test properties of any voting method whose choice of winners depends only on the ordering of margins of victory by size.

With the growing importance of dermoscopic analysis to diagnose skin diseases, the vocabulary of dermoscopy has rapidly expanded without standardized control. Many metaphoric terms are ambiguous and create barriers for general research and education. Ontologies are computable artifacts that represent and model information from a domain space that can later be leveraged by software to understand domain knowledge. We aim to standardize dermoscopic vocabulary through the introduction of two ontologies: 1. the Elements of Visuals Ontology (EVO), a foundational ontology to decompose the visual elements of physical entities; and 2. the Dermoscopy Elements of Visuals Ontology (DEVO), a domain ontology that harnesses EVO to formalize the definitions of dermoscopic metaphoric terms. We discuss how DEVO would enhance both trainee education and patient care, with the future goal of generating responses to queries about dermoscopic features and integrating these features with diagnostic rules for skin diseases.