Developing and modelling complex social interventions: introducing the Connecting People Intervention

Objectives: Modeling the processes involved in complex social interventions is important in social work practice, as it facilitates their implementation and translation into different contexts. This article reports the process of developing and modeling the connecting people intervention (CPI), a model of practice that supports people with mental health problems to enhance their social networks. Method: The CPI model was developed through an iterative process of focus group discussions with practitioners and service users and a two-stage Delphi consultation with relevant experts. Results: We discuss the intervention model and the processes it articulates to provide an example of the benefits of intervention modeling. Conclusions: Intervention modeling provides a visual representation of the process and outcomes of an intervention, which can assist practice development and lead to improved outcomes for service users

Disjunctive Logic Programs with Inheritance

The paper proposes a new knowledge representation language, called DLP<, which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP< is provided, which is shown to generalize the Answer Set Semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP<. The complexity of DLP< is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP< has exactly the same complexity as reasoning in disjunctive logic programming. This is confirmed by the existence of an efficient translation from DLP< to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP< language has been implemented on top of the DLV system and has subsequently been integrated into DLV.Comment: 28 pages; will be published in Theory and Practice of Logic Programmin

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

Risk evaluation using evolvable discriminate function

This essay proposes a new approach to risk evaluation using disease mathematical modeling. The mathematical model is an algebraic equation of the available database attributes and is used to evaluate the patient condition. If its value is greater than zero it means that the patient is ill (or in risk condition), otherwise healthy. In practice risk evaluation has been a very difficult problem mainly due its sporadic behavior (suddenly, the patient has a stroke, etc as a condition aggravation) and its database representation. The database contains, under the label of risk patient data, information of the patient condition that sometimes is in risk condition and sometimes is not, introducing errors in the algorithm training. The study was applied to Atherosclerosis database from Discovery Challenge 2003 - ECML/PKDD 2003 workshop

Elements of a Theory of Simulation

Unlike computation or the numerical analysis of differential equations, simulation does not have a well established conceptual and mathematical foundation. Simulation is an arguable unique union of modeling and computation. However, simulation also qualifies as a separate species of system representation with its own motivations, characteristics, and implications. This work outlines how simulation can be rooted in mathematics and shows which properties some of the elements of such a mathematical framework has. The properties of simulation are described and analyzed in terms of properties of dynamical systems. It is shown how and why a simulation produces emergent behavior and why the analysis of the dynamics of the system being simulated always is an analysis of emergent phenomena. A notion of a universal simulator and the definition of simulatability is proposed. This allows a description of conditions under which simulations can distribute update functions over system components, thereby determining simulatability. The connection between the notion of simulatability and the notion of computability is defined and the concepts are distinguished. The basis of practical detection methods for determining effectively non-simulatable systems in practice is presented. The conceptual framework is illustrated through examples from molecular self-assembly end engineering.Comment: Also available via http://studguppy.tsasa.lanl.gov/research_team/ Keywords: simulatability, computability, dynamics, emergence, system representation, universal simulato