1,721,044 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
A new Defragmentation Algorithm for Dynamic Optical Networks
No full textTraffic requests are becoming more heterogeneous and dynamic causing the so-called spectrum fragmentation. We propose a reactive defragmentation algorithm to mitigate this issue which degrades the network performance. Three strategies are suggested to compact the spectrum comb, taking into account the minimization of the disrupted services and their reconfiguration times. An accurate Rerouting strategy allows the set-up of a higher amount of traffic, obtaining more capacitive networks
Variations on the Author
Full text link“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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Regeneration savings in coherent optical networks with a new load-dependent reach maximization
No full textWe propose a new load-dependent reach maximization procedure in dispersion-uncompensated optical networks with coherent detection, and estimate the electro-optic regenerations savings with respect to the standard full-load reach approach
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Use of neural networks for the retrieval of water parameters from hyperspectral data
No full textSome indicators of water quality parameters such as chlorophyll-a, suspended sediments and dissolved organic matter, alter, depending on their concentration, the optical properties of water, influencing, in this way, what is the signal spectrum
measured by a remote sensor. Multi-spectral sensors like MERIS and MODIS are often used to estimate the 'bio-optical' parameters in oceanic and coastal waters. Nevertheless, it is difficult, using multi-spectral sensors, to estimate accurately the concentrations of water constituents, because of the wide bandwidth and the few number of bands of these sensors. With the availability of hyper-spectral data able to distinguish significant features of the spectral signature of water mass, it became possible to estimate much more accurately the concentrations of chlorophyll-a, suspended sediment and organic matter dissolved in the water. However, two types of problems have to be managed for the design of an effective retrieval procedure. On the one hand it has to be taken into account that the inverse problem maybe rather complex due to the high nonlinearities characterizing it. On the other hand, the high dimensionality of the hyperspectral measurements suggests that appropriate feature extraction techniques have to be considered in a pre-processing stage. Neural networks can be a possible solution for both the aforementioned issues. Indeed neural algorithms, besides having been proven a suitable approach for discovering subtle nonlinearities in multi-dimensional data, can also be considered for feature extraction problems. In this latter case a particular architecture called Autoassociative Neural Networks can be used. The AANN topology includes an internal
“bottleneck” layer and is trained in order that the input layer is approximated at the output layer. If the training phase is
satisfactorily completed, the bottleneck nodes must represent or encode the information in the inputs for the subsequent
layers. In this work, the potential of neural networks algorithms for the retrieval of water quality parameters from hyperspectral data has been investigated. A simulated dataset based on 6S – (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer model has been generated. The data were used for the design of a double stage neural architecture
where in the first stage a features reduction algorithm has been performed by means of AANN, in the second stage the
reduced measurement vector has been used as input to an inversion NN scheme for the quantitative retrieval of water
quality parameters. The data set has been split into a training and a testing set. The first one has been exploited for
determining the networks adaptive parameters while the second one for evaluating their performance. A final validation based on experimental Hyperion data has been also considered
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