OOREA: An Object-Oriented Resources, Events, Agents Model for Enterprise Systems Design

A number of modeling approaches have been proposed in the literature for designing business information systems. This paper critiques prior data modeling approaches and presents an integrated object-oriented modeling approach that captures both the structural and the behavioral aspects of the business domain. Although there is considerable interest in object-oriented (OO) technologies in practice and in the information systems literature, there is no widely accepted OO modeling approach that facilitates the identification of objects from a business information processing perspective. Based on McCarthy’s (1982) resources, events, agents (REA) framework, the business process focused object-oriented ontology presented in this paper identifies the key resources, events, and agents in an enterprise information systems context. Termed OOREA, the ontology extends McCarthy’s REA model by capturing both the structural aspects of modeling, in terms of the objects of interest in the domain, and also the behavioral aspects in terms of the processes that modify objects. Application of the model is illustrated in the context of sales and related events for a retailing enterprise

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.Comment: 9 pages, 5 figure

Low-Lying Neutron-Hole Transitions in the 207-Pb(p,p') Reaction at 135 MeV

This work was supported by National Science Foundation Grant PHY 75-00289 and Indiana Universit

Transitions to Proton States in the 90-Zr(p,p') Reaction at 160 MeV

This work was supported by National Science Foundation Grant PHY 76-84033 and Indiana Universit

Spin-Orbit Effects on the Shapes of Cross Sections in the 90-Zr(p,p') Reaction at 160 MeV

This work was supported by National Science Foundation Grants PHY 76-84033A01, PHY 78-22774, and Indiana Universit

Excitation of Neutron, Proton and Neutron-Hole States in the (p,p') Reaction at 160 MeV and 96 MeV

This work was supported by National Science Foundation Grant PHY 76-84033 and Indiana Universit

Core Polarization Amplitudes for Single-Neutron-Hole Transitions Excited in the 207-Pb(p,p') Reaction at 135 MeV and 61 MeV

This work was supported by National Science Foundation Grants PHY 76-84033A01, PHY 78-22774, and Indiana Universit

Low-Lying Transitions in the 207-Pb(p,p') Reaction at 135 MeV and a Test of the DWIA

This work was supported by National Science Foundation Grant PHY 76-84033 and Indiana Universit