54,614 research outputs found
Relationships between Models for Concurrency
Models for concurrency can be classified with respect to three relevant parameters: behaviour/system, interleaving/noninterleaving, linear/branching time. When modelling a process, a choice concerning such parameters corresponds to choosing the level of abstraction of the resulting semantics. The classifications are formalized through the medium of category theory
A Classification of Models for Concurrency
Models for concurrency can be classified with respect to the three relevant parameters: behaviour/system, interleaving/noninterleaving, linear/branching time. When modelling a process, a choice concerning such parameters corresponds to choosing the level of abstraction of the resulting semantics. The classifications are formalised through the medium of category theory
Deterministic Behavioural Models for Concurrency
This paper offers three candidates for a deterministic, noninterleaving, behaviour model which generalizes Hoare traces to the noninterleaving situation. The three models are all proved equivalent in the rather strong sense of being equivalent as categories. The models are: deterministic labelled event structures, generalized trace languages in which the independence relation is context-dependent, and deterministic languages of pomsets
New Host Records of Parasitic Hymenoptera in Clearwing Moths (Lepidoptera: Sesiidae)
Synanthedon pictipes is host for Dibrachus cavus; Podosesia aureocincta hosts Phaeogenes ater, Macrocentrus marginator, and Lissonota sp.; Podosesia syringae hosts Lissonota sp
Learning Markov Decision Processes for Model Checking
Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345
Enhancement of Entanglement Percolation in Quantum Networks via Lattice Transformations
We study strategies for establishing long-distance entanglement in quantum
networks. Specifically, we consider networks consisting of regular lattices of
nodes, in which the nearest neighbors share a pure, but non-maximally entangled
pair of qubits. We look for strategies that use local operations and classical
communication. We compare the classical entanglement percolation protocol, in
which every network connection is converted with a certain probability to a
singlet, with protocols in which classical entanglement percolation is preceded
by measurements designed to transform the lattice structure in a way that
enhances entanglement percolation. We analyze five examples of such comparisons
between protocols and point out certain rules and regularities in their
performance as a function of degree of entanglement and choice of operations.Comment: 12 pages, 17 figures, revtex4. changes from v3: minor stylistic
changes for journal reviewer, minor changes to figures for journal edito
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