1,721,065 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Real-world load time series
No full textIn this chapter, we consider three different real-world datasets, which contain real-valued time series of measurements of electricity and telephonic activity load. For each dataset, we set up a short-term load forecast problem of 24 hours ahead prediction. Two of the datasets under analysis include time series of measurements of exogenous variables, which are used to provide additional context to the network and thus to improve the accuracy of the prediction. For each dataset, we perform an analysis to study the nature of the time series, in terms of its correlation properties, seasonal patterns, correlation with the exogenous time series, and nature of the variance. According to the result of our analysis, we select a suitable preprocessing strategy before feeding the data into the recurrent neural networks. As shown in the following, the forecast accuracy in a prediction problem can be considerably improved by proper preprocessing of data (Zhang and Qi 2005)
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Introduction
No full textA short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of utmost importance for the planning of facilities, optimization of day-to-day operations, and an effective management of the available resources. In the context of energy and telecommunication networks, the load data are usually represented as real-valued time series characterized by strong temporal dependencies and seasonal patterns. We begin by reviewing several methods that have been adopted in the past years for the task of short-term load forecast and we highlight their main advantages and limitations. We then introduce the framework of recurrent neural networks, a particular class of artificial neural networks specialized in the processing of sequential/temporal data. We explain how recurrent neural networks can be an effective tool for prediction, especially in those cases where the extent of the time dependencies is unknown a-priori
Conclusions
No full textIn this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand
Recurrent neural network architectures
No full textIn this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure. First, we present the most basic version of recurrent neural networks, called Elman recurrent neural network. Then, we introduce two popular gated architectures, which are long short-term memory and the gated recurrent units. We discuss the main advantages of these more sophisticated architectures, especially regarding their capability to process much longer dependencies in time by maintaining an internal memory for longer periods. For each one of the reviewed network, we provide the details and we show the equations for updating the internal state and computing the output at each time step. Then, for each recurrent neural network we also provide a quick overview of its main applications in previous works in the context of real-valued time series forecasting
Synthetic time series
No full textIn this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly considered in the literature to evaluate the performance of a predictive model. The three forecasting exercises that we study have varying levels of difficulty, given by the nature of the signal and the complexity of the task to be solved by the RNN
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Properties and training in recurrent neural networks
No full textIn this chapter, we describe the basic concepts behind the functioning of recurrent neural networks and explain the general properties that are common to several existing architectures. We introduce the basis of their training procedure, the backpropagation through time, as a general way to propagate and distribute the prediction error to previous states of the network. The learning procedure consists of updating the model parameters by minimizing a suitable loss function, which includes the error achieved on the target task and, usually, also one or more regularization terms. We then discuss several ways of regularizing the system, highlighting their advantages and drawbacks. Beside the standard stochastic gradient descent procedure, we also present several additional optimization strategies proposed in the literature for updating the network weights. Finally, we illustrate the problem of the vanishing gradient effect, an inherent problem of the gradient-based optimization techniques which occur in several situations while training neural networks. We conclude by discussing the most recent and successful approaches proposed in the literature to limit the vanishing of the gradients
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