We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman's fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.
Previous studies aimed to disclose the functional organization of the neuronal networks involved in the generation of the spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments used predetermined templates to select specific classes of spontaneous CDPs. Since this procedure was time consuming and required continuous supervision, it was limited to the analysis of two specific types of CDPs (negative CDPs and negative positive CDPs), thus excluding potentials that may reflect activation of other neuronal networks of presumed functional relevance. We now present a novel procedure based in machine learning that allows the efficient and unbiased selection of a variety of spontaneous CDPs with different shapes and amplitudes. The reliability and performance of the present method is evaluated by analyzing the effects on the probabilities of generation of different classes of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat, a procedure known to induce a state of central sensitization leading to allodynia and hyperalgesia. The results obtained with the selection method presently described allowed detection of spontaneous CDPs with specific shapes and amplitudes that are assumed to represent the activation of functionally coupled sets of dorsal horn neurones that acquire different, structured configurations in response to nociceptive stimuli. These changes are considered as responses tending to adequate transmission of sensory information to specific functional requirements as part of homeostatic adjustments.
ÐStatistical research in clustering has almost universally focused on data sets described by continuous features and its methods are difficult to apply to tasks involving symbolic features. In addition, these methods are seldom concerned with helping the user in interpreting the results obtained. Machine learning researchers have developed conceptual clustering methods aimed at solving these problems. Following a long term tradition in AI, early conceptual clustering implementations employed logic as the mechanism of concept representation. However, logical representations have been criticized for constraining the resulting cluster structures to be described by necessary and sufficient conditions. An alternative are probabilistic concepts which associate a probability or weight with each property of the concept definition. In this paper, we propose a symbolic hierarchical clustering model that makes use of probabilistic representations and extends the traditional ideas of specificity-generality typically found in machine learning. We propose a parameterized measure that allows users to specify both the number of levels and the degree of generality of each level. By providing some feedback to the user about the balance of the generality of the concepts created at each level and given the intuitive behavior of the user parameter, the system improves user interaction in the clustering process.
Despite a profusion of information on the molecular and cellular mechanisms involved in the central sensitization produced by intense nociceptive stimulation, the changes in the patterns of functional connectivity between spinal neurones associated with the development of secondary hyperalgesia and allodynia remain largely unknown. Here we show that the state of central sensitization produced by the intradermal injection of capsaicin is associated with structured transformations in neuronal synchronization that lead to an enduring reorganization of the functional connectivity within a segmentally distributed ensemble of dorsal horn neurones. These changes are transiently reversed by the systemic administration of small doses of lidocaine, a clinically effective procedure to treat neuropathic pain. Lidocaine also reduces the capsaicin-induced facilitation of the spinal responses evoked by weak mechanical stimulation of the skin in the region of secondary but not primary hyperalgesia. The effects of both intradermic capsaicin and systemic lidocaine on the segmental correlation and coherence between ongoing cord dorsum potentials and on the responses evoked by tactile stimulation in the region of secondary hyperalgesia are greatly attenuated in spinalized preparations, showing that supraspinal influences are involved in the reorganization of the nociceptive-induced structured patterns of dorsal horn neuronal connectivity. We conclude that the structured reorganization of the functional connectivity between the dorsal horn neurones induced by capsaicin nociceptive stimulation results from cooperative interactions between supraspinal and spinal networks, a process that may have a relevant role in the shaping of the spinal state in the pathogenesis of chronic pain and analgesia.
To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.
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