6,331 research outputs found
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
From types to type requirements: Genericity for model-driven engineering
The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-011-0221-0Model-driven engineering (MDE) is a software engineering paradigm that proposes an active use of models during the development process. This paradigm is inherently type-centric, in the sense that models and their manipulation are defined over the types of specific meta-models. This fact hinders the reuse of existing MDE artefacts with other meta-models in new contexts, even if all these meta-models share common characteristics. To increase the reuse opportunities of MDE artefacts, we propose a paradigm shift from type-centric to requirement-centric specifications by bringing genericity into models, meta-models and model management operations. For this purpose, we introduce so-called concepts gathering structural and behavioural requirements for models and meta-models. In this way, model management operations are defined over concepts, enabling the application of the operations to any meta-model satisfying the requirements imposed by the concept. Model templates rely on concepts to define suitable interfaces, hence enabling the definition of reusable model components. Finally, similar to mixin layers, templates can be defined at the meta-model level as well, to define languages in a modular way, as well as layers of functionality to be plugged-in into other meta-models. These ideas have been implemented in MetaDepth, a multi-level meta-modelling tool that integrates action languages from the Epsilon family for model management and code generation.This work has been sponsored by the Spanish Ministry of Science and Innovation with projects METEORIC (TIN2008-02081) and Go Lite (TIN2011-24139), and by the R&D program of the Community of Madrid with project “e-Madrid” (S2009/TIC-1650)
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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
Acoustic characterization of speech rhythm: going beyond metrics with recurrent neural networks
Languages have long been described according to their perceived rhythmic
attributes. The associated typologies are of interest in psycholinguistics as
they partly predict newborns' abilities to discriminate between languages and
provide insights into how adult listeners process non-native languages. Despite
the relative success of rhythm metrics in supporting the existence of
linguistic rhythmic classes, quantitative studies have yet to capture the full
complexity of temporal regularities associated with speech rhythm. We argue
that deep learning offers a powerful pattern-recognition approach to advance
the characterization of the acoustic bases of speech rhythm. To explore this
hypothesis, we trained a medium-sized recurrent neural network on a language
identification task over a large database of speech recordings in 21 languages.
The network had access to the amplitude envelopes and a variable identifying
the voiced segments, assuming that this signal would poorly convey phonetic
information but preserve prosodic features. The network was able to identify
the language of 10-second recordings in 40% of the cases, and the language was
in the top-3 guesses in two-thirds of the cases. Visualization methods show
that representations built from the network activations are consistent with
speech rhythm typologies, although the resulting maps are more complex than two
separated clusters between stress and syllable-timed languages. We further
analyzed the model by identifying correlations between network activations and
known speech rhythm metrics. The findings illustrate the potential of deep
learning tools to advance our understanding of speech rhythm through the
identification and exploration of linguistically relevant acoustic feature
spaces.Comment: 15 pages, 7 figure
Timed pushdown automata revisited
This paper contains two results on timed extensions of pushdown automata
(PDA). As our first result we prove that the model of dense-timed PDA of
Abdulla et al. collapses: it is expressively equivalent to dense-timed PDA with
timeless stack. Motivated by this result, we advocate the framework of
first-order definable PDA, a specialization of PDA in sets with atoms, as the
right setting to define and investigate timed extensions of PDA. The general
model obtained in this way is Turing complete. As our second result we prove
NEXPTIME upper complexity bound for the non-emptiness problem for an expressive
subclass. As a byproduct, we obtain a tight EXPTIME complexity bound for a more
restrictive subclass of PDA with timeless stack, thus subsuming the complexity
bound known for dense-timed PDA.Comment: full technical report of LICS'15 pape
Quantitative Regular Expressions for Arrhythmia Detection Algorithms
Motivated by the problem of verifying the correctness of arrhythmia-detection
algorithms, we present a formalization of these algorithms in the language of
Quantitative Regular Expressions. QREs are a flexible formal language for
specifying complex numerical queries over data streams, with provable runtime
and memory consumption guarantees. The medical-device algorithms of interest
include peak detection (where a peak in a cardiac signal indicates a heartbeat)
and various discriminators, each of which uses a feature of the cardiac signal
to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms'
desired output in current temporal logics, and implementing them via monitor
synthesis, is cumbersome, error-prone, computationally expensive, and sometimes
infeasible.
In contrast, we show that a range of peak detectors (in both the time and
wavelet domains) and various discriminators at the heart of today's
arrhythmia-detection devices are easily expressible in QREs. The fact that one
formalism (QREs) is used to describe the desired end-to-end operation of an
arrhythmia detector opens the way to formal analysis and rigorous testing of
these detectors' correctness and performance. Such analysis could alleviate the
regulatory burden on device developers when modifying their algorithms. The
performance of the peak-detection QREs is demonstrated by running them on real
patient data, on which they yield results on par with those provided by a
cardiologist.Comment: CMSB 2017: 15th Conference on Computational Methods for Systems
Biolog
Entropy Games and Matrix Multiplication Games
Two intimately related new classes of games are introduced and studied:
entropy games (EGs) and matrix multiplication games (MMGs). An EG is played on
a finite arena by two-and-a-half players: Despot, Tribune and the
non-deterministic People. Despot wants to make the set of possible People's
behaviors as small as possible, while Tribune wants to make it as large as
possible.An MMG is played by two players that alternately write matrices from
some predefined finite sets. One wants to maximize the growth rate of the
product, and the other to minimize it. We show that in general MMGs are
undecidable in quite a strong sense.On the positive side, EGs correspond to a
subclass of MMGs, and we prove that such MMGs and EGs are determined, and that
the optimal strategies are simple. The complexity of solving such games is in
NP\&coNP.Comment: Accepted to STACS 201
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