AI can design extremely dense protein shells that could one day lead to more potent vaccines. The genetic material of viruses is housed in protein shells. Similar shells made in the lab are used in vaccines, encapsulating molecules that induce an immune response in the body. The chemical and biological properties of these artificially made shells depend on their construction – any imperfections in them, no matter how small, make them less effective, causing them to be unstable and react unpredictably inside cells. Isaac Lutz at the University of Washington in Seattle and his colleagues wanted to see if using artificial intelligence could make the design and creation of these shells more precise.

In ontology-based data access (OBDA), ontologies are used as an interface for querying instance data. Since in typical applications the size of the data is much larger than the size of the ontology and query, data complexity is the most important complexity measure. In this paper, we propose a new method for investigating data complexity in OBDA: instead of classifying whole logics according to their complexity, we aim at classifying each individual ontology within a given master language. Our results include a P/coNP-dichotomy theorem for ontologies of depth one in the description logic ALCFI, the equivalence of a P/coNP-dichotomy theorem for ALC/ALCI-ontologies of unrestricted depth to the famous dichotomy conjecture for CSPs by Feder and Vardi, and a non-P/coNP-dichotomy theorem for ALCF-ontologies.

In the context of ontology-based data access with description logics (DLs), we study ontology-mediated queries in which selected predicates can be closed (OMQCs). In particular, we contribute to the classification of the data complexity of such queries in several relevant DLs. For the case where only concept names can be closed, we tightly link this question to the complexity of surjective CSPs. When also role names can be closed, we show that a full complexity classification is equivalent to classifying the complexity of all problems in coNP, thus currently out of reach. We also identify a class of OMQCs based on ontologies formulated in DL-LiteR that are guaranteed to be tractable and even FO-rewritable.

We present a unified approach to (both finite and unrestricted) worst-case optimal entailment of (unions of) conjunctive queries (U)CQs in the wide class of "locally-forward" description logics. The main technique that we employ is a generalisation of Lutz's spoiler technique, originally developed for CQ entailment in ALCHQ. Our result closes numerous gaps present in the literature, most notably implying ExpTime-completeness of (U)CQ-querying for any superlogic of ALC contained in ALCHbregQ, and, as we believe, is abstract enough to be employed as a black-box in many new scenarios.

A Dutch couple have become Europe's first tenants of a fully 3D printed house in a development that its backers believe will open up a world of choice in the shape and style of the homes of the future. Elize Lutz, 70, and Harrie Dekkers, 67, retired shopkeepers from Amsterdam, received their digital key – an app allowing them to open the front door of their two-bedroom bungalow at the press of a button – on Thursday. "It is beautiful," said Lutz. "It has the feel of a bunker – it feels safe," added Dekkers. Inspired by the shape of a boulder, the dimensions of which would be difficult and expensive to construct using traditional methods, the property is the first of five homes planned by the construction firm Saint-Gobain Weber Beamix for a plot of land by the Beatrix canal in the Eindhoven suburb of Bosrijk. In the last two years properties partly constructed by 3D printing have been built in France and the US, and nascent projects are proliferating around the world.

Thanks to the advent of player-tracking data in the NBA and the use of machine learning software running on powerful servers, we're on the cusp of having some fouls called automatically in professional basketball. But that is just the beginning of what AI can do in the NBA, according to Dwight Lutz, the senior director of basketball strategy and analytics for the Atlanta Hawks. In a virtual talk presented by The Society of HPC Professionals on Friday, Lutz says we're very close to having an AI referee that can call one specific foul: a defensive three-second (D3S) violation. Unless a defensive player is guarding an offensive player, or attempting a rebound, he is not allowed to be in the lane for more than three seconds, which is a rule the NBA instituted in 2001 to speed up game play and bolster offensive excitement. You can thank the maturation of the NBA's player tracking system, which was first implemented for the 2013-2014 season, for the advent of AI refs in the NBA.

We investigate the decidability and computational complexity of conservative extensions and the related notions of inseparability and entailment in Horn description logics (DLs) with inverse roles. We consider both query conservative extensions, defined by requiring that the answers to all conjunctive queries are left unchanged, and deductive conservative extensions, which require that the entailed concept inclusions, role inclusions, and functionality assertions do not change. Upper bounds for query conservative extensions are particularly challenging because characterizations in terms of unbounded homomorphisms between universal models, which are the foundation of the standard approach to establishing decidability, fail in the presence of inverse roles. We resort to a characterization that carefully mixes unbounded and bounded homomorphisms and enables a decision procedure that combines tree automata and a mosaic technique. Our main results are that query conservative extensions are 2ExpTime-complete in all DLs between ELI and Horn-ALCHIF and between Horn-ALC and Horn-ALCHIF, and that deductive conservative extensions are 2ExpTime-complete in all DLs between ELI and ELHIF_\bot. The same results hold for inseparability and entailment.

We consider ontology-mediated queries (OMQs) based on expressive description logics of the ALC family and (unions) of conjunctive queries, studying the rewritability into OMQs based on instance queries (IQs). Our results include exact characterizations of when such a rewriting is possible and tight complexity bounds for deciding rewritability. We also give a tight complexity bound for the related problem of deciding whether a given MMSNP sentence is equivalent to a CSP.

We investigate the decidability and computational complexity of conservative extensions and the related notions of inseparability and entailment in Horn description logics (DLs) with inverse roles. We consider both query conservative extensions, defined by requiring that the answers to all conjunctive queries are left unchanged, and deductive conservative extensions, which require that the entailed concept inclusions, role inclusions, and functionality assertions do not change. Upper bounds for query conservative extensions are particularly challenging because characterizations in terms of unbounded homomorphisms between universal models, which are the foundation of the standard approach to establishing decidability, fail in the presence of inverse roles. We resort to a characterization that carefully mixes unbounded and bounded homomorphisms and enables a decision procedure that combines tree automata and a mosaic technique. Our main results are that query conservative extensions are 2ExpTime-complete in all DLs between ELI and Horn-ALCHIF and between Horn-ALC and Horn-ALCHIF, and that deductive conservative extensions are 2ExpTime-complete in all DLs between ELI and ELHIF_bot. The same results hold for inseparability and entailment.

March 12, 2017 --In traditional computers, the smallest units of information exists in one of two states: 1 and 0, or on and off. Long strings of 1s and 0s can store increasingly complex information that can be used use to perform useful tasks, but that information storage is limited by the size of those individual bits of information in a computer's hard drive. But now, researchers have figured out a way to magnetically store information on the smallest unit possible: a single atom. There's a long way to go before atom-sized information storage technology can make it to your home computer or smartphone, but now researchers have proven that it is possible to store information on an incredibly small level. Theoretically, this new technology could lead to massive data storage capacities on an impressive scale – even in the smallest of devices.