Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables

Toroidal crossings and logarithmic structures

We generalize Friedman's notion of d-semistability, which is a necessary condition for spaces with normal crossings to admit smoothings with regular total space. Our generalization deals with spaces that locally look like the boundary divisor in Gorenstein toroidal embeddings. In this situation, we replace d-semistability by the existence of global log structures for a given gerbe of local log structures. This leads to cohomological descriptions for the obstructions, existence, and automorphisms of log structures. We also apply toroidal crossings to mirror symmetry, by giving a duality construction involving toroidal crossing varieties whose irreducible components are toric varieties. This duality reproduces a version of Batyrev's construction of mirror pairs for hypersurfaces in toric varieties, but it applies to a larger class, including degenerate abelian varieties.Comment: 34 pages, 1 figure, notational changes, to appear in Adv. Mat

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is a CNN-based category agnostic object location refinement module that is capable of yielding accurate and robust bounding box predictions regardless of the object category. We extensively evaluate our AttractioNet approach on several image datasets (i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories. Furthermore, we evaluate our AttractioNet proposals in the context of the object detection task using a VGG16-Net based detector and the achieved detection performance on COCO manages to significantly surpass all other VGG16-Net based detectors while even being competitive with a heavily tuned ResNet-101 based detector. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNet.Comment: Technical report. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNe

Comparing Poynting flux dominated magnetic tower jets with kinetic-energy dominated jets

Magnetic Towers represent one of two fundamental forms of MHD outflows. Driven by magnetic pressure gradients, these flows have been less well studied than magneto-centrifugally launched jets even though magnetic towers may well be as common. Here we present new results exploring the behavior and evolution of magnetic tower outflows and demonstrate their connection with pulsed power experimental studies and purely hydrodynamic jets which might represent the asymptotic propagation regimes of magneto-centrifugally launched jets. High-resolution AMR MHD simulations (using the AstroBEAR code) provide insights into the underlying physics of magnetic towers and help us constrain models of their propagation. Our simulations have been designed to explore the effects of thermal energy losses and rotation on both tower flows and their hydro counterparts. We find these parameters have significant effects on the stability of magnetic towers, but mild effects on the stability of hydro jets. Current-driven perturbations in the Poynting Flux Dominated (PDF) towers are shown to be amplified in both the cooling and rotating cases. Our studies of the long term evolution of the towers show that the formation of weakly magnetized central jets within the tower are broken up by these instabilities becoming a series of collimated clumps which magnetization properties vary over time. In addition to discussing these results in light of laboratory experiments, we address their relevance to astrophysical observations of young star jets and outflow from highly evolved solar type stars.Comment: 11 pages, 4 figures, accepted for publication in the High Energy Density Physics Journal corresponding to the proceedings of the 9th International Conference on High Energy Density Laboratory Astrophysics, May 4, 2012, Tallahassee Florid