158,320 research outputs found

    Highly comparative feature-based time-series classification

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    A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation

    Output-based Aid for Sustainable Sanitation

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    A review of the experience to date in applying output-based and other results-oriented financing aid formats to the delivery of sanitation services and goods in developing countries. The paper looks at the theoretical underpinnings which justify output-based subsidies in sanitation, reviews a selection of output-based aid projects and then proposes some new approaches which could help to make financing in sanitation more effective and accountable

    Development of Auditory Selective Attention: Why Children Struggle to Hear in Noisy Environments

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    Children’s hearing deteriorates markedly in the presence of unpredictable noise. To explore why, 187 school-age children (4–11 years) and 15 adults performed a tone-in-noise detection task, in which the masking noise varied randomly between every presentation. Selective attention was evaluated by measuring the degree to which listeners were influenced by (i.e., gave weight to) each spectral region of the stimulus. Psychometric fits were also used to estimate levels of internal noise and bias. Levels of masking were found to decrease with age, becoming adult-like by 9–11 years. This change was explained by improvements in selective attention alone, with older listeners better able to ignore noise similar in frequency to the target. Consistent with this, age-related differences in masking were abolished when the noise was made more distant in frequency to the target. This work offers novel evidence that improvements in selective attention are critical for the normal development of auditory judgments

    The Role of Response Bias in Perceptual Learning

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    Sensory judgments improve with practice. Such perceptual learning is often thought to reflect an increase in perceptual sensitivity. However, it may also represent a decrease in response bias, with unpracticed observers acting in part on a priori hunches rather than sensory evidence. To examine whether this is the case, 55 observers practiced making a basic auditory judgment (yes/no amplitude-modulation detection or forced-choice frequency/amplitude discrimination) over multiple days. With all tasks, bias was present initially, but decreased with practice. Notably, this was the case even on supposedly “bias-free,” 2-alternative forced-choice, tasks. In those tasks, observers did not favor the same response throughout (stationary bias), but did favor whichever response had been correct on previous trials (nonstationary bias). Means of correcting for bias are described. When applied, these showed that at least 13% of perceptual learning on a forced-choice task was due to reduction in bias. In other situations, changes in bias were shown to obscure the true extent of learning, with changes in estimated sensitivity increasing once bias was corrected for. The possible causes of bias and the implications for our understanding of perceptual learning are discussed

    Development of pigments for thermal control coatings Final report, 17 Jun. - 16 Dec. 1965

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    Powdered metal oxide pigments by nucleation for temperature control coating
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