7,080 research outputs found
Intersection schemas as a dataspace integration technique
This paper introduces the concept of Intersection Schemas in the field of heterogeneous data integration and dataspaces. We introduce a technique for incrementally integrating heterogeneous data sources by specifying semantic overlaps between sets of extensional schemas using bidirectional schema transformations, and automatically combining them into a global schema at each iteration of the integration process. We propose an incremental data integration methodology that uses this technique and that aims to reduce the amount of up-front effort required. Such approaches to data integration are often described as pay-as-you-go. A demonstrator of our technique is described, which utilizes a new graphical user tool implemented using the AutoMed
heterogeneous data integration system. A case study is also described, and our technique and integration methodology
are compared with a classical data integration strategy
The Efficacy of Group Selection is Increased by Coexistence Dynamics within Groups
Selection on the level of loosely associated groups has been suggested as a route towards the evolution of cooperation between individuals and the subsequent formation of higher-level biological entities. Such group selection explanations remain problematic, however, due to the narrow range of parameters under which they can overturn within-group selection that favours selfish behaviour. In principle, individual selection could act on such parameters so as to strengthen the force of between-group selection and hence increase cooperation and individual fitness, as illustrated in our previous work. However, such a process cannot operate in parameter regions where group selection effects are totally absent, since there would be no selective gradient to follow. One key parameter, which when increased often rapidly causes group selection effects to tend to zero, is initial group size, for when groups are formed randomly then even moderately sized groups lack significant variance in their composition. However, the consequent restriction of any group selection effect to small sized groups is derived from models that assume selfish types will competitively exclude their more cooperative counterparts at within-group equilibrium. In such cases, diversity in the migrant pool can tend to zero and accordingly variance in group composition cannot be generated. In contrast, we show that if within-group dynamics lead to a stable coexistence of selfish and cooperative types, then the range of group sizes showing some effect of group selection is much larger
The UK Consumer's Attitudes to, and Willingness to Pay for, imported Foods
We report results from an investigation into consumer preferences for locally produced foods. Using a choice experiment we estimate willingness to pay for foods of a designated origin together with certification for Organic and GM free status. Our results indicate that there is a preference for locally produced food which is GM free, Organic and produced in the traditional season.imported food, seasonality, willingness-to-pay, choice experiment, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, International Relations/Trade,
Clay pipes from Port Arthur 1830-1877: a descriptive account of the clay pipes from Maureen Byrne's 1977-78 excavations at Port Arthur, Southeast Tasmania
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb
In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st
Multidisciplinary Conference on Reinforcement Learning and Decision Making,
Princeton, NJ, USA, Oct. 25-27, 201
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