11,584 research outputs found
Flux Analysis in Process Models via Causality
We present an approach for flux analysis in process algebra models of
biological systems. We perceive flux as the flow of resources in stochastic
simulations. We resort to an established correspondence between event
structures, a broadly recognised model of concurrency, and state transitions of
process models, seen as Petri nets. We show that we can this way extract the
causal resource dependencies in simulations between individual state
transitions as partial orders of events. We propose transformations on the
partial orders that provide means for further analysis, and introduce a
software tool, which implements these ideas. By means of an example of a
published model of the Rho GTP-binding proteins, we argue that this approach
can provide the substitute for flux analysis techniques on ordinary
differential equation models within the stochastic setting of process algebras
Neural nets on the MPP
The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman
Representing Conversations for Scalable Overhearing
Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent applications. Recently, Petri nets have been shown to be an
interesting approach to such representation, and radically different approaches
using Petri nets have been proposed. However, their relative strengths and
weaknesses have not been examined. Moreover, their scalability and suitability
for different tasks have not been addressed. This paper addresses both these
challenges. First, we analyze existing Petri net representations in terms of
their scalability and appropriateness for overhearing, an important task in
monitoring open multi-agent systems. Then, building on the insights gained, we
introduce a novel representation using Colored Petri nets that explicitly
represent legal joint conversation states and messages. This representation
approach offers significant improvements in scalability and is particularly
suitable for overhearing. Furthermore, we show that this new representation
offers a comprehensive coverage of all conversation features of FIPA
conversation standards. We also present a procedure for transforming AUML
conversation protocol diagrams (a standard human-readable representation), to
our Colored Petri net representation
Estimating Parameters of a Renewable Resource Model Without Population Data
A general approach to determining parameters of a traditional bioeconomic model is offered for the situation in which knowledge of resource abundance is unknown. Production parameters (such as catchability coefficients) and biological factors (such as natural mortality and recruitment) are included in the model. The general model is articulated for a typical fishery and further specified to obtain estimates of parameters for the St. John's River shad fishery. The results, considering the illustrative nature of the analysis, are promising and suggest avenues of additional research.Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Risk and Uncertainty,
Start Time and Duration Distribution Estimation in Semi-Structured Processes
Semi-structured processes are business workflows, where the execution of the workflow is not completely controlled by a workflow engine, i.e., an implementation of a formal workflow model. Examples are workflows where actors potentially have interaction with customers reporting the result of the interaction in a process aware information system. Building a performance model for resource management in these processes is difficult since the required information is only partially recorded. In this paper we propose a systematic approach for the creation of an event log that is suitable for available process mining tools. This event log is created by an incrementally cleansing of data. The proposed approach is evaluated in an experiment
Programmability of Chemical Reaction Networks
Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri Nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior
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