925 resultados para Artificial Intelligence and Robotics


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Multiplication and comultiplication of beliefs represent a generalisation of multiplication and comultiplication of probabilities as well as of binary logic AND and OR. Our approach follows that of subjective logic, where belief functions are expressed as opinions that are interpreted as being equivalent to beta probability distributions. We compare different types of opinion product and coproduct, and show that they represent very good approximations of the analytical product and coproduct of beta probability distributions. We also define division and codivision of opinions, and compare our framework with other logic frameworks for combining uncertain propositions. (C) 2004 Elsevier Inc. All rights reserved.

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There is ongoing debate whether the efficiency of local cognitive processes leads to global cognitive ability or whether global ability feeds the efficiency of basic processes. A prominent example is the well-replicated association between inspection time (IT), a measure of perceptual discrimination speed, and intelligence (IQ), where it is not known whether increased speed is a cause or consequence of high IQ. We investigated the direction of causation between IT and IQ in 2012 genetically related subjects from Australia and The Netherlands. Models in which the reliable variance of each observed variable was specified as a latent trait showed IT correlations of -0.44 and -0.33 with respective Performance and Verbal IQ; heritabilities were 57% (IT), 83% (PIQ) and 77% (VIQ). Directional causation models provided poor fits to the data, with covariation best explained by pleiotropic genes (influencing variation in both IT and IQ). This finding of a common genetic factor provides a better target for identifying genes involved in cognition than genes which are unique to specific traits.

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This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).