Sentence examples for deep inference from inspiring English sources

Deep inference provides a richer combinatoric analysis of proofs for different logics.

We give Maude implementations of deep inference systems together with an implementation that simulates sequent calculus proofs to serve as a benchmark.

We demonstrate these ideas on classical logic, and argue that they can be analogously carried to other deep inference systems for other logics, as well as sequent calculus systems.

In this paper, aiming at the development of computation as proof search tools, we propose the Maude language as a means for designing and implementing different deep inference deductive systems and proof strategies that work on these systems.

The intron/exon structure also allows us to generate deeper inferences regarding interclade relationships (see Discussion).

When the GPUs are idle, they run mining to pay the hardware providers, and Snark AI also offers the capability to both mine and run deep learning inference on a piece of hardware simultaneously, Buniatyan said.

The predictions themselves are elaborated on the basis of deep hierarchical inference about states of the world, including the trajectories of our own bodies.

In order to model covariation in rRNA sequences, we estimated secondary structure interactions by applying a new approach implemented in the software RNAsalsa [ 56] (download available from ), which helps to accommodate inadequate modeling (e.g. missing covariotide effects) of rRNA substitution processes in deep phylogenetic inference [ 34, 57].

While it has been demonstrated that genetic data based on repetitive elements are not appropriate for deep phylogenetic inference [ 5], the observation that a VNTR type can be identical by convergent evolution indicates that classifying isolates based on VNTR type may lead to erroneous epidemiological conclusions regarding transmission events occurring within a timeframe of a few years.

"You've got cars, you've got drones, you've got microphones; in the future, almost every electronic device will have some form of deep learning inferencing within it.

Open source databases are ensuring that the results of studies are widely accessible, and we expect that pooling of resources will also facilitate mega-analyses that provide opportunities for deeper statistical inference.