24,680 research outputs found
Logical Conceptualization of Knowledge on the Notion of Language Communication
The main objective of the paper is to provide a conceptual apparatus
of a general logical theory of language communication. The aim of the paper is
to outline a formal-logical theory of language in which the concepts of the phenomenon
of language communication and language communication in general
are defined and some conditions for their adequacy are formulated. The theory
explicates the key notions of contemporary syntax, semantics, and pragmatics.
The theory is formalized on two levels: token-level and type-level. As such, it
takes into account the dual – token and type – ontological character of linguistic
entities. The basic notions of the theory: language communication, meaning
and interpretation are introduced on the second, type-level of formalization, and
their required prior formalization of some of the notions introduced on the first,
token-level; among others, the notion of an act of communication. Owing to the
theory, it is possible to address the problems of adequacy of both empirical acts
of communication and of language communication in general. All the conditions
of adequacy of communication discussed in the presented paper, are valid for
one-way communication (sender-recipient); nevertheless, they can also apply to
the reverse direction of language communication (recipient-sender). Therefore,
they concern the problem of two-way understanding in language communication
Abridged Petri Nets
A new graphical framework, Abridged Petri Nets (APNs) is introduced for
bottom-up modeling of complex stochastic systems. APNs are similar to
Stochastic Petri Nets (SPNs) in as much as they both rely on component-based
representation of system state space, in contrast to Markov chains that
explicitly model the states of an entire system. In both frameworks, so-called
tokens (denoted as small circles) represent individual entities comprising the
system; however, SPN graphs contain two distinct types of nodes (called places
and transitions) with transitions serving the purpose of routing tokens among
places. As a result, a pair of place nodes in SPNs can be linked to each other
only via a transient stop, a transition node. In contrast, APN graphs link
place nodes directly by arcs (transitions), similar to state space diagrams for
Markov chains, and separate transition nodes are not needed.
Tokens in APN are distinct and have labels that can assume both discrete
values ("colors") and continuous values ("ages"), both of which can change
during simulation. Component interactions are modeled in APNs using triggers,
which are either inhibitors or enablers (the inhibitors' opposites).
Hierarchical construction of APNs rely on using stacks (layers) of submodels
with automatically matching color policies. As a result, APNs provide at least
the same modeling power as SPNs, but, as demonstrated by means of several
examples, the resulting models are often more compact and transparent,
therefore facilitating more efficient performance evaluation of complex
systems.Comment: 17 figure
The Information-Flow Approach to Ontology-Based Semantic Integration
In this article we argue for the lack of formal foundations for ontology-based semantic alignment. We analyse and formalise the basic notions of semantic matching and alignment and we situate them in the context of ontology-based alignment in open-ended and distributed environments, like the Web. We then use the mathematical notion of information flow in a distributed system to ground three hypotheses that enable semantic alignment. We draw our exemplar applications of this work from a variety of interoperability scenarios including ontology mapping, theory of semantic interoperability, progressive ontology alignment, and situated semantic alignment
Generic Pipelined Processor Modeling and High Performance Cycle-Accurate Simulator Generation
Detailed modeling of processors and high performance cycle-accurate
simulators are essential for today's hardware and software design. These
problems are challenging enough by themselves and have seen many previous
research efforts. Addressing both simultaneously is even more challenging, with
many existing approaches focusing on one over another. In this paper, we
propose the Reduced Colored Petri Net (RCPN) model that has two advantages:
first, it offers a very simple and intuitive way of modeling pipelined
processors; second, it can generate high performance cycle-accurate simulators.
RCPN benefits from all the useful features of Colored Petri Nets without
suffering from their exponential growth in complexity. RCPN processor models
are very intuitive since they are a mirror image of the processor pipeline
block diagram. Furthermore, in our experiments on the generated cycle-accurate
simulators for XScale and StrongArm processor models, we achieved an order of
magnitude (~15 times) speedup over the popular SimpleScalar ARM simulator.Comment: Submitted on behalf of EDAA (http://www.edaa.com/
Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams
Authors of biomedical publications use gel images to report experimental
results such as protein-protein interactions or protein expressions under
different conditions. Gel images offer a concise way to communicate such
findings, not all of which need to be explicitly discussed in the article text.
This fact together with the abundance of gel images and their shared common
patterns makes them prime candidates for automated image mining and parsing. We
introduce an approach for the detection of gel images, and present a workflow
to analyze them. We are able to detect gel segments and panels at high
accuracy, and present preliminary results for the identification of gene names
in these images. While we cannot provide a complete solution at this point, we
present evidence that this kind of image mining is feasible.Comment: arXiv admin note: substantial text overlap with arXiv:1209.148
Progressive Ontology Alignment for Meaning Coordination: an Information-Theoretic Foundation
We elaborate on the mathematical foundations of the meaning coordination problem that agents face in open environments. We investigate to which extend the Barwise-Seligman theory of information flow provides a faithful theoretical description of the partial semantic integration that two agents achieve as they progressively align their underlying ontologies through the sharing of tokens, such as instances. We also discuss the insights and practical implications of the Barwise-Seligman theory with respect to the general meaning coordination proble
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