Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

The Supersymmetric Fine-Tuning Problem and TeV-Scale Exotic Scalars

A general framework is presented for supersymmetric theories that do not suffer from fine-tuning in electroweak symmetry breaking. Supersymmetry is dynamically broken at a scale \Lambda \approx (10 - 100) TeV, which is transmitted to the supersymmetric standard model sector through standard model gauge interactions. The dynamical supersymmetry breaking sector possesses an approximate global SU(5) symmetry, whose SU(3) x SU(2) x U(1) subgroup is explicitly gauged and identified as the standard model gauge group. This SU(5) symmetry is dynamically broken at the scale \Lambda, leading to pseudo-Goldstone boson states, which we call xyons. We perform a detailed estimate for the xyon mass and find that it is naturally in the multi-TeV region. We study general properties of xyons, including their lifetime, and study their collider signatures. A generic signature is highly ionizing tracks caused by stable charged bound states of xyons, which may be observed at the LHC. We also consider cosmology in our scenario and find that a consistent picture can be obtained. Our framework is general and does not depend on the detailed structure of the Higgs sector, nor on the mechanism of gaugino mass generation.Comment: 53 pages, 7 figure

On the Complexity of Rule Discovery from Distributed Data

This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and confidence, known from association rule mining. A common strategy to tackle rule selection from distributed data is to evaluate rules locally on each dataset. While this works well for homogeneously distributed data, this work proves limitations of this strategy if distributions are allowed to deviate. To identify those subsets for which local and global distributions deviate may be regarded as an interesting learning task of its own, explicitly taking the locality of data into account. This task can be shown to be basically as complex as discovering the globally best rules from local data. Based on the theoretical results some guidelines for algorithm design are derived. --

Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO$_2$ to fuels and other useful chemicals. We demonstrate that an electron transfer to the $\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO$_2$ activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find more

Isomorphisms between big mapping class groups

We show that any isomorphism between mapping class groups of orientable infinite-type surfaces is induced by a homeomorphism between the surfaces. Our argument additionally applies to automorphisms between finite-index subgroups of these `big' mapping class groups and shows that each finite-index subgroup has finite outer automorphism group. As a key ingredient, we prove that all simplicial automorphisms between curve complexes of infinite-type orientable surfaces are induced by homeomorphisms.Comment: v3: 11 pages; updated and added references; final version to appear in IMR

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201