Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

An overview of Mirjam and WeaveC

In this chapter, we elaborate on the design of an industrial-strength aspectoriented programming language and weaver for large-scale software development. First, we present an analysis on the requirements of a general purpose aspect-oriented language that can handle crosscutting concerns in ASML software. We also outline a strategy on working with aspects in large-scale software development processes. In our design, we both re-use existing aspect-oriented language abstractions and propose new ones to address the issues that we identified in our analysis. The quality of the code ensured by the realized language and weaver has a positive impact both on maintenance effort and lead-time in the first line software development process. As evidence, we present a short evaluation of the language and weaver as applied today in the software development process of ASML

The Place of the Trace: Negligence and Responsibility

One popular theory of moral responsibility locates responsible agency in exercises of control. These control-based theories often appeal to tracing to explain responsibility in cases where some agent is intuitively responsible for bringing about some outcome despite lacking direct control over that outcome’s obtaining. Some question whether control-based theories are committed to utilizing tracing to explain responsibility in certain cases. I argue that reflecting on certain kinds of negligence shows that tracing plays an ineliminable role in any adequate control-based theory of responsibility

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (1-R29-DC02952-01); Office of Naval Research (N00014-92-J-1309, N00014-01-1-0624); Air Force Office of Scientific Research (F49620-01-1-0397); National Institute of Neurological Disorders and Stroke (NS 33173

A Tracing Method for Pricing Inter-Area Electricity Trades

In the context of liberalisation of electricity markets world wide, the need for agreed protocols for electricity trades between systems with different charges poses a special challenge. System operators need to know how much a given trade uses the network, in order to allocate an appropriate portion of their costs to that trade. This paper discusses a technique, tracing, for determining how much each of a number of trades uses different parts of the electricity network. The scheme is based on the assumption that at any network node, inflows are shared proportionally between outflows (and vice versa). The paper outlines the technique and shows how it could be applied to the problem of charging cross-border trades. The paper goes on to demonstrate that the technique has a game theoretic rationale, in that it produces the Shapley value solution to a game equivalent to this allocation problem

Evaluation of the Multiplane Method for Efficient Simulations of Reaction Networks

Reaction networks in the bulk and on surfaces are widespread in physical, chemical and biological systems. In macroscopic systems, which include large populations of reactive species, stochastic fluctuations are negligible and the reaction rates can be evaluated using rate equations. However, many physical systems are partitioned into microscopic domains, where the number of molecules in each domain is small and fluctuations are strong. Under these conditions, the simulation of reaction networks requires stochastic methods such as direct integration of the master equation. However, direct integration of the master equation is infeasible for complex networks, because the number of equations proliferates as the number of reactive species increases. Recently, the multiplane method, which provides a dramatic reduction in the number of equations, was introduced [A. Lipshtat and O. Biham, Phys. Rev. Lett. 93, 170601 (2004)]. The reduction is achieved by breaking the network into a set of maximal fully connected sub-networks (maximal cliques). Lower-dimensional master equations are constructed for the marginal probability distributions associated with the cliques, with suitable couplings between them. In this paper we test the multiplane method and examine its applicability. We show that the method is accurate in the limit of small domains, where fluctuations are strong. It thus provides an efficient framework for the stochastic simulation of complex reaction networks with strong fluctuations, for which rate equations fail and direct integration of the master equation is infeasible. The method also applies in the case of large domains, where it converges to the rate equation results