Twisted hyperk\"ahler symmetries and hyperholomorphic line bundles

In this paper we propose and investigate in full generality new notions of (continuous, non-isometric) symmetry on hyperk\"ahler spaces. These can be grouped into two categories, corresponding to the two basic types of continuous hyperk\"ahler isometries which they deform: tri-Hamiltonian isometries, on one hand, and rotational isometries, on the other. The first category of deformations gives rise to Killing spinors and generate what are known as hidden hyperk\"ahler symmetries. The second category gives rise to hyperholomorphic line bundles over the hyperk\"ahler manifolds on which they are defined and, by way of the Atiyah-Ward correspondence, to holomorphic line bundles over their twistor spaces endowed with meromorphic connections, generalizing similar structures found in the purely rotational case by Haydys and Hitchin. Examples of hyperk\"ahler metrics with this type of symmetry include the c-map metrics on cotangent bundles of affine special K\"ahler manifolds with generic prepotential function, and the hyperk\"ahler constructions on the total spaces of certain integrable systems proposed by Gaiotto, Moore and Neitzke in connection with the wall-crossing formulas of Kontsevich and Soibelman, to which our investigations add a new layer of geometric understanding.Comment: 95 pages. v3: With an extended introduction. To appear in the Journal of Geometry and Physic

Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively.Comment: 4 pages, presented at the Human in the Loop workshop at ICML 201