62,248 research outputs found
Word-level Symbolic Trajectory Evaluation
Symbolic trajectory evaluation (STE) is a model checking technique that has
been successfully used to verify industrial designs. Existing implementations
of STE, however, reason at the level of bits, allowing signals to take values
in {0, 1, X}. This limits the amount of abstraction that can be achieved, and
presents inherent limitations to scaling. The main contribution of this paper
is to show how much more abstract lattices can be derived automatically from
RTL descriptions, and how a model checker for the general theory of STE
instantiated with such abstract lattices can be implemented in practice. This
gives us the first practical word-level STE engine, called STEWord. Experiments
on a set of designs similar to those used in industry show that STEWord scales
better than word-level BMC and also bit-level STE.Comment: 19 pages, 3 figures, 2 tables, full version of paper in International
Conference on Computer-Aided Verification (CAV) 201
Detecting event-related recurrences by symbolic analysis: Applications to human language processing
Quasistationarity is ubiquitous in complex dynamical systems. In brain
dynamics there is ample evidence that event-related potentials reflect such
quasistationary states. In order to detect them from time series, several
segmentation techniques have been proposed. In this study we elaborate a recent
approach for detecting quasistationary states as recurrence domains by means of
recurrence analysis and subsequent symbolisation methods. As a result,
recurrence domains are obtained as partition cells that can be further aligned
and unified for different realisations. We address two pertinent problems of
contemporary recurrence analysis and present possible solutions for them.Comment: 24 pages, 6 figures. Draft version to appear in Proc Royal Soc
The engineering design integration (EDIN) system
A digital computer program complex for the evaluation of aerospace vehicle preliminary designs is described. The system consists of a Univac 1100 series computer and peripherals using the Exec 8 operating system, a set of demand access terminals of the alphanumeric and graphics types, and a library of independent computer programs. Modification of the partial run streams, data base maintenance and construction, and control of program sequencing are provided by a data manipulation program called the DLG processor. The executive control of library program execution is performed by the Univac Exec 8 operating system through a user established run stream. A combination of demand and batch operations is employed in the evaluation of preliminary designs. Applications accomplished with the EDIN system are described
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
We summarize the accomplishments of a multi-disciplinary workshop exploring
the computational and scientific issues surrounding the discovery of linguistic
units (subwords and words) in a language without orthography. We study the
replacement of orthographic transcriptions by images and/or translated text in
a well-resourced language to help unsupervised discovery from raw speech.Comment: Accepted to ICASSP 201
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