230,469 research outputs found
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
Making Random Choices Invisible to the Scheduler
When dealing with process calculi and automata which express both
nondeterministic and probabilistic behavior, it is customary to introduce the
notion of scheduler to solve the nondeterminism. It has been observed that for
certain applications, notably those in security, the scheduler needs to be
restricted so not to reveal the outcome of the protocol's random choices, or
otherwise the model of adversary would be too strong even for ``obviously
correct'' protocols. We propose a process-algebraic framework in which the
control on the scheduler can be specified in syntactic terms, and we show how
to apply it to solve the problem mentioned above. We also consider the
definition of (probabilistic) may and must preorders, and we show that they are
precongruences with respect to the restricted schedulers. Furthermore, we show
that all the operators of the language, except replication, distribute over
probabilistic summation, which is a useful property for verification
Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data
In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy
Structural operational semantics for stochastic and weighted transition systems
We introduce weighted GSOS, a general syntactic framework to specify well-behaved transition systems where transitions are equipped with weights coming from a commutative monoid. We prove that weighted bisimilarity is a congruence on systems defined by weighted GSOS specifications. We illustrate the flexibility of the framework by instantiating it to handle some special cases, most notably that of stochastic transition systems. Through examples we provide weighted-GSOS definitions for common stochastic operators in the literature
Model Checking Markov Chains with Actions and State Labels
In the past, logics of several kinds have been proposed for reasoning about discrete- or continuous-time Markov chains. Most of these logics rely on either state labels (atomic propositions) or on transition labels (actions). However, in several applications it is useful to reason about both state-properties and action-sequences. For this purpose, we introduce the logic asCSL which provides powerful means to characterize execution paths of Markov chains with actions and state labels. asCSL can be regarded as an extension of the purely state-based logic asCSL (continuous stochastic logic). \ud
In asCSL, path properties are characterized by regular expressions over actions and state-formulas. Thus, the truth value of path-formulas does not only depend on the available actions in a given time interval, but also on the validity of certain state formulas in intermediate states.\ud
We compare the expressive power of CSL and asCSL and show that even the state-based fragment of asCSL is strictly more expressive than CSL if time intervals starting at zero are employed. Using an automaton-based technique, an asCSL formula and a Markov chain with actions and state labels are combined into a product Markov chain. For time intervals starting at zero we establish a reduction of the model checking problem for asCSL to CSL model checking on this product Markov chain. The usefulness of our approach is illustrated by through an elaborate model of a scalable cellular communication system for which several properties are formalized by means of asCSL-formulas, and checked using the new procedure
Distributive Laws for Monotone Specifications
Turi and Plotkin introduced an elegant approach to structural operational
semantics based on universal coalgebra, parametric in the type of syntax and
the type of behaviour. Their framework includes abstract GSOS, a categorical
generalisation of the classical GSOS rule format, as well as its categorical
dual, coGSOS. Both formats are well behaved, in the sense that each
specification has a unique model on which behavioural equivalence is a
congruence. Unfortunately, the combination of the two formats does not feature
these desirable properties. We show that monotone specifications - that
disallow negative premises - do induce a canonical distributive law of a monad
over a comonad, and therefore a unique, compositional interpretation.Comment: In Proceedings EXPRESS/SOS 2017, arXiv:1709.0004
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