Decision Analysis via Granulation Based on General Binary Relation

Decision theory considers how best to make decisions in the light of uncertainty about data. There are several methodologies that may be used to determine the best decision. In rough set theory, the classification of objects according to approximation operators can be fitted into the Bayesian decision-theoretic model, with respect to three regions (positive, negative, and boundary region). Granulation using equivalence classes is a restriction that limits the decision makers. In this paper, we introduce a generalization and modification of decision-theoretic rough set model by using granular computing on general binary relations. We obtain two new types of approximation that enable us to classify the objects into five regions instead of three regions. The classification of decision region into five areas will enlarge the range of choice for decision makers

Knowledge structure, knowledge granulation and knowledge distance in a knowledge base

AbstractOne of the strengths of rough set theory is the fact that an unknown target concept can be approximately characterized by existing knowledge structures in a knowledge base. Knowledge structures in knowledge bases have two categories: complete and incomplete. In this paper, through uniformly expressing these two kinds of knowledge structures, we first address four operators on a knowledge base, which are adequate for generating new knowledge structures through using known knowledge structures. Then, an axiom definition of knowledge granulation in knowledge bases is presented, under which some existing knowledge granulations become its special forms. Finally, we introduce the concept of a knowledge distance for calculating the difference between two knowledge structures in the same knowledge base. Noting that the knowledge distance satisfies the three properties of a distance space on all knowledge structures induced by a given universe. These results will be very helpful for knowledge discovery from knowledge bases and significant for establishing a framework of granular computing in knowledge bases

Astrophysical Insights into Radial Velocity Jitter from an Analysis of 600 Planet-search Stars

Radial velocity (RV) detection of planets is hampered by astrophysical processes on the surfaces of stars that induce a stochastic signal, or "jitter," which can drown out or even mimic planetary signals. Here, we empirically and carefully measure the RV jitter of more than 600 stars from the California Planet Search sample on a star by star basis. As part of this process, we explore the activity–RV correlation of stellar cycles and include appendices listing every ostensibly companion-induced signal we removed and every activity cycle we noted. We then use precise stellar properties from Brewer et al. to separate the sample into bins of stellar mass and examine trends with activity and with evolutionary state. We find that RV jitter tracks stellar evolution and that in general, stars evolve through different stages of RV jitter: the jitter in younger stars is driven by magnetic activity, while the jitter in older stars is convectively driven and dominated by granulation and oscillations. We identify the "jitter minimum"—where activity-driven and convectively driven jitter have similar amplitudes—for stars between 0.7 and 1.7 M⊙ and find that more-massive stars reach this jitter minimum later in their lifetime, in the subgiant or even giant phases. Finally, we comment on how these results can inform future RV efforts, from prioritization of follow-up targets from transit surveys like TESS to target selection of future RV surveys

Asteroseismic determination of obliquities of the exoplanet systems Kepler-50 and Kepler-65

Results on the obliquity of exoplanet host stars -- the angle between the stellar spin axis and the planetary orbital axis -- provide important diagnostic information for theories describing planetary formation. Here we present the first application of asteroseismology to the problem of stellar obliquity determination in systems with transiting planets and Sun-like host stars. We consider two systems observed by the NASA Kepler Mission which have multiple transiting small (super-Earth sized) planets: the previously reported Kepler-50 and a new system, Kepler-65, whose planets we validate in this paper. Both stars show rich spectra of solar-like oscillations. From the asteroseismic analysis we find that each host has its rotation axis nearly perpendicular to the line of sight with the sines of the angles constrained at the 1-sigma level to lie above 0.97 and 0.91, respectively. We use statistical arguments to show that coplanar orbits are favoured in both systems, and that the orientations of the planetary orbits and the stellar rotation axis are correlated.Comment: Accepted for publication in ApJ; 46 pages, 11 figure

Discrete Mathematics and Symmetry

Some of the most beautiful studies in Mathematics are related to Symmetry and Geometry. For this reason, we select here some contributions about such aspects and Discrete Geometry. As we know, Symmetry in a system means invariance of its elements under conditions of transformations. When we consider network structures, symmetry means invariance of adjacency of nodes under the permutations of node set. The graph isomorphism is an equivalence relation on the set of graphs. Therefore, it partitions the class of all graphs into equivalence classes. The underlying idea of isomorphism is that some objects have the same structure if we omit the individual character of their components. A set of graphs isomorphic to each other is denominated as an isomorphism class of graphs. The automorphism of a graph will be an isomorphism from G onto itself. The family of all automorphisms of a graph G is a permutation group