5,409 research outputs found
MatchPy: A Pattern Matching Library
Pattern matching is a powerful tool for symbolic computations, based on the
well-defined theory of term rewriting systems. Application domains include
algebraic expressions, abstract syntax trees, and XML and JSON data.
Unfortunately, no lightweight implementation of pattern matching as general and
flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt.
Therefore, we created the open source module MatchPy which offers similar
pattern matching functionality in Python using a novel algorithm which finds
matches for large pattern sets more efficiently by exploiting similarities
between patterns.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0007
Communicative success in spatial dialogue: The impact of functional features and dialogue strategies
This paper addresses the impact of dialogue strategies and functional features of spatial arrangements on communicative success. To examine the sharing of cognition between two minds in order to achieve a joint goal, we collected a corpus of 24 extended German-language dialogues in a referential communication task that involved furnishing a dolls’ house. Results show how successful communication, as evidenced by correct placement of furniture items, is affected by a) functionality of the furniture arrangement, b) previous task experience, and c) dialogue features such as description length and orientation information. To enhance research in this area, our 'Dolldialogue' corpus is now available as a free resource on www.dolldialogue.spac
Locally Non-rigid Registration for Mobile HDR Photography
Image registration for stack-based HDR photography is challenging. If not
properly accounted for, camera motion and scene changes result in artifacts in
the composite image. Unfortunately, existing methods to address this problem
are either accurate, but too slow for mobile devices, or fast, but prone to
failing. We propose a method that fills this void: our approach is extremely
fast---under 700ms on a commercial tablet for a pair of 5MP images---and
prevents the artifacts that arise from insufficient registration quality
Adults are more efficient in creating and transmitting novel signalling systems than children
Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5-7-year old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4-5-year old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems is unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution
Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars
Model synchronization, i.e., the task of restoring consistency between two
interrelated models after a model change, is a challenging task. Triple Graph
Grammars (TGGs) specify model consistency by means of rules that describe how
to create consistent pairs of models. These rules can be used to automatically
derive further rules, which describe how to propagate changes from one model to
the other or how to change one model in such a way that propagation is
guaranteed to be possible. Restricting model synchronization to these derived
rules, however, may lead to unnecessary deletion and recreation of model
elements during change propagation. This is inefficient and may cause
unnecessary information loss, i.e., when deleted elements contain information
that is not represented in the second model, this information cannot be
recovered easily. Short-cut rules have recently been developed to avoid
unnecessary information loss by reusing existing model elements. In this paper,
we show how to automatically derive (short-cut) repair rules from short-cut
rules to propagate changes such that information loss is avoided and model
synchronization is accelerated. The key ingredients of our rule-based model
synchronization process are these repair rules and an incremental pattern
matcher informing about suitable applications of them. We prove the termination
and the correctness of this synchronization process and discuss its
completeness. As a proof of concept, we have implemented this synchronization
process in eMoflon, a state-of-the-art model transformation tool with inherent
support of bidirectionality. Our evaluation shows that repair processes based
on (short-cut) repair rules have considerably decreased information loss and
improved performance compared to former model synchronization processes based
on TGGs.Comment: 33 pages, 20 figures, 3 table
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