28,426 research outputs found
On Generative Parallel Composition
A major reason for studying probabilistic processes is to establish a link between a formal model for describing functional system behaviour and a stochastic process. Compositionality is an essential ingredient for specifying systems. Parallel composition in a probabilistic setting is complicated since it gives rise to non-determinism, for instance due to interleaving of independent autonomous activities. This paper presents a detailed study of the resolution of non-determinism in an asynchronous generative setting. Based on the intuition behind the synchronous probabilistic calculus PCCS we formulate two criteria that an asynchronous parallel composition should fulfill. We provide novel probabilistic variants of parallel composition for CCS and CSP and show that these operators satisfy these general criteria, opposed to most existing proposals. Probabilistic bisimulation is shown to be a congruence for these operators and their expansion is addressed.\ud
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We would like to thank the reviewers for their constructive criticism and for pointing out the relation between BPTSs and the model of Pnueli and Zuck. We also thank Ed Brinksma and Rom Langerak (both of the University of Twente) for fruitful discussions
A Faster-Than Relation for Semi-Markov Decision Processes
When modeling concurrent or cyber-physical systems, non-functional
requirements such as time are important to consider. In order to improve the
timing aspects of a model, it is necessary to have some notion of what it means
for a process to be faster than another, which can guide the stepwise
refinement of the model. To this end we study a faster-than relation for
semi-Markov decision processes and compare it to standard notions for relating
systems. We consider the compositional aspects of this relation, and show that
the faster-than relation is not a precongruence with respect to parallel
composition, hence giving rise to so-called parallel timing anomalies. We take
the first steps toward understanding this problem by identifying decidable
conditions sufficient to avoid parallel timing anomalies in the absence of
non-determinism.Comment: In Proceedings QAPL 2019, arXiv:2001.0616
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.Comment: In Proceedings of the Semantic Parsing Workshop at ACL 2014
(forthcoming
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