CPN Tools 4: Multi-formalism and Extensibility

Abstract. CPN Tools is an advanced tool for editing, simulating, and analyzing colored Petri nets. This paper discusses the fourth major re-lease of the tool, which makes it simple to use the tool for ordinary Petri nets, including adding inhibitor and reset arcs, and PNML export. This version also supports declarative modeling using constraints, and adds an extension framework making it easy for third parties to extend CPN Tools using Java.

Giant capacitance of a plane capacitor with a two-dimensional electron gas in a magnetic field

If a clean two-dimensional electron gas (2DEG) with small concentration $n$ comprises one (or both) electrodes of a plane capacitor, the resulting capacitance $C$ can be larger than the "geometric capacitance" $C_g$ determined by the physical separation $d$ between electrodes. A recent paper [1] argued that when the effective Bohr radius $a_B$ of the 2DEG satisfies $a_B << d$, one can achieve $C >> C_g$ at low concentration $nd^2 << 1$. Here we show that even for devices with $a_B > d$, including graphene, for which $a_B$ is effectively infinite, one also arrives at $C >> C_g$ at low electron concentration if there is a strong perpendicular magnetic field.Comment: 6 pages, 5 figures; updated discussion about bilayer systems; added discussion of fractional quantum Hall state

CPN Tools 4 : a process modeling tool combining declarative and imperative paradigms

CPN Tools is a tool for modeling, simulating, and analyzing colored Petri nets. The latest iteration of the tool, CPN Tools 4, extends this with constraints known from declarative languages such as Declare and DCR Graphs. Furthermore, this version introduces an explicit process perspective, powerful extensibility allowing third parties to extend the tools capabilities, and a visualization perspective making it possible to make high-level visualizations of executions directly in the tool. In our demonstration, we show how it is possible to create models incorporating declarative and imperative constructs and how to use these models to generate simulation logs that can be directly imported into ProM. We show o¿ the new process perspective on top of colored Petri nets, exemplify the use of the perspective to generate readable Java code directly from models, and show how the visualization perspective makes it possible to show the formal underlying model alongside an easier-to-grasp for non-experts high-level visualization. Our intended audience comprise current users of CPN Tools interested in recent developments and practitioners interested in colored Petri nets and hybrid models. We expect to tailor each demonstration to the wishes of the audience

Mixing Paradigms for More Comprehensible Models

Petri nets efficiently model both data- and control-flow. Control-flow is either modeled explicitly as flow of a specific kind of data, or implicit based on the data-flow. Explicit modeling of control-flow is useful for well-known and highly structured processes, but may make modeling of abstract features of models, or processes which are highly dynamic, overly complex. Declarative modeling, such as is supported by Declare and DCR graphs, focus on control-flow, but does not specify it explicitly; instead specifications come in the form of constraints on the order or appearance of tasks. In this paper we propose a combination of the two, using colored Petri nets instead of plain Petri nets to provide full data support. The combined approach makes it possible to add a focus on data to declarative languages, and to remove focus from the explicit control-flow from Petri nets for dynamic or abstract processes. In addition to enriching both procedural processes in the form of Petri nets and declarative processes, we also support a flow from modeling only abstract data- and control-flow of a model towards a more explicit control-flow model if so desired. We define our combined approach, and provide considerations necessary for enactment. Our approach has been implemented in CPN Tools 4

UnconstrainedMiner : efficient discovery of generalized declarative process models

Process discovery techniques derive a process model from observed behavior (e.g., event logs). In case of less structured processes, declarative models have notable advantages over procedural models. A declarative model consists of a set of temporal constraints over the activities in the event log. In this paper, we address three limitations of current discovery techniques: their unclear semantics of declarative constraints for business processes, their non-performative discovery of constraints, and their potential identification of vacuous constraints. We implemented our contributions as a declarative discovery algorithm for the Declare language. Our evaluations on a real-life event log indicate that it outperforms state of the art techniques by several orders of magnitude

Ultrastable lasers based on vibration insensitive cavities

We present two ultra-stable lasers based on two vibration insensitive cavity designs, one with vertical optical axis geometry, the other horizontal. Ultra-stable cavities are constructed with fused silica mirror substrates, shown to decrease the thermal noise limit, in order to improve the frequency stability over previous designs. Vibration sensitivity components measured are equal to or better than 1.5e-11 per m.s^-2 for each spatial direction, which shows significant improvement over previous studies. We have tested the very low dependence on the position of the cavity support points, in order to establish that our designs eliminate the need for fine tuning to achieve extremely low vibration sensitivity. Relative frequency measurements show that at least one of the stabilized lasers has a stability better than 5.6e-16 at 1 second, which is the best result obtained for this length of cavity.Comment: 8 pages 12 figure

An Infrastructure for Cost-Effective Testing of Operational Support Algorithms Based on Colored Petri Nets

Operational support is a specific type of process mining that assists users while process instances are being executed. Examples are predicting the remaining processing time of a running insurance claim and recommending the action that minimizes the treatment costs of a particular patient. Whereas it is easy to evaluate prediction techniques using cross validation, the evaluation of recommendation techniques is challenging as the recommender influences the execution of the process. It is therefore impossible to simply use historic event data. Therefore, we present an approach where we use a colored Petri net model of user behavior to drive a real workflow system and real implementations of operational support, thereby providing a way of evaluating algorithms for operational support before implementation and a costly test using real users. In this paper, we evaluate algorithms for operational support using different user models. We have implemented our approach using Access/CPN 2.0