1,720,976 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
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
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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
Tid, rum och kontroll: djupinlärningsapplikationer för turbulenta flöden
No full textIn the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. Long short-term memory (LSTM, Hochreiter & Schmidhuber 1997) networks and Koopman non-linear forcing (KNF) models are optimized to perform temporal predictions in two reduced-order-models of turbulence, namely the nine-equations model proposed by Moehlis et al. (2004) and a truncated proper orthogonal decomposition (POD) of a minimal channel flow (Jiménez & Moin 1991). In the first application, both models are able to produce accurate short-term predictions. Furthermore, the predicted system trajectories are statistically correct. KNF models outperform LSTM networks in short-term predictions, with a much lower training computational cost. In the second task, only LSTMs can be trained successfully, predicting trajectories that are statistically accurate. Spatial predictions are performed in two turbulent flows: an open channel flow and a boundary-layer flow. Fully-convolutional networks (FCNs) are used to predict two-dimensional velocity-fluctuation fields at a given wall-normal location using wall measurements (and vice versa). Thanks to the non-linear nature of these models, they provide a better reconstruction performance than optimal linear methods like extended POD (Borée 2003). Finally, we show the potential of deep reinforcement learning in discovering new control strategies for turbulent flows. By framing the fluid-dynamics problem as a multi-agent reinforcement-learning environment and by training the agents using a location-invariant deep deterministic policy-gradient (DDPG) algorithm, we are able to learn a control strategy that achieves a remarkable 30% drag reduction, improving over existing strategies by about 10 percentage points.I den förinställda avhandlingen undersöks tillämpningen av djupinlärning och djupförstärkningsinlärning på turbulenta flödessimuleringar. Modeller för djupinlärning tränas för att utföra tids- och rumsförutsägelser, medan djupförstärkningsinlärning tillämpas på ett flödeskontrollproblem, nämligen minskningen av motståndet i ett öppet kanalflöde. Long short-term memory (LSTM, Hochreiter & Schmidhuber 1997) nätverk och Koopman non-linear forcing (KNF) modeller optimeras för att utföratidsförutsägelser i två turbulensmodeller med reducerad ordning, nämligen nio-ekvationsmodellen som föreslagits av Moehlis et al. (2004) och en trunkerad proper orthogonal decomposition (POD) av ett minimalt kanalflöde (Jiménez & Moin 1991). I den första applikationen kan båda modellerna producera korrekta korttidsförutsägelser, dessutom är de förutsagda trajektorierna statistiskt korrekta. KNF-modeller överträffar LSTM-nätverk i kortsiktiga förutsägelser, med en mycket lägre utbildningsberäkningskostnad. I den andra uppgiften kan endast LSTM nätverken tränas framgångsrikt, med trajektorier som är statistiskt korrekta. Spatiala förutsägelser utförs i två turbulenta flöden, en öppen kanal flöde och en gränsskikt. Fully-convolutional networks (FCN) används för att förutsäga tvådimensionella hastighetsfluktuationsfält vid givet avstånd från väggen med hjälp av väggmätningar (och vice versa). Tack vare deras icke-linjär karaktär ger dessa modeller bättre rekonstruktionsprestanda än optimala linjära metoder som extended POD (Borée 2003). Slutligen visar vi potentialen med djup förstärkningsinlärning för att upptäcka nya kontrollstrategier i turbulenta flöden. Genom att inrama strömningsmekaniska problemet som en förstärknings-inlärningsmiljö med flera agenter och genom att träna agenterna med hjälp av en positionsinvariant deep deterministic policy gradient (DDPG) algoritm, kan vi lära oss en kontrollstrategi som uppnår en anmärkningsvärd 30% minskning av luftmotståndet, vilket jämfört med existerande strategier är en förbättring med cirka 10 procentenheter.QC 230516</p
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Tid, rum och kontroll: djupinlärningsapplikationer för turbulenta flöden
No full textIn the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. Long short-term memory (LSTM, Hochreiter & Schmidhuber 1997) networks and Koopman non-linear forcing (KNF) models are optimized to perform temporal predictions in two reduced-order-models of turbulence, namely the nine-equations model proposed by Moehlis et al. (2004) and a truncated proper orthogonal decomposition (POD) of a minimal channel flow (Jiménez & Moin 1991). In the first application, both models are able to produce accurate short-term predictions. Furthermore, the predicted system trajectories are statistically correct. KNF models outperform LSTM networks in short-term predictions, with a much lower training computational cost. In the second task, only LSTMs can be trained successfully, predicting trajectories that are statistically accurate. Spatial predictions are performed in two turbulent flows: an open channel flow and a boundary-layer flow. Fully-convolutional networks (FCNs) are used to predict two-dimensional velocity-fluctuation fields at a given wall-normal location using wall measurements (and vice versa). Thanks to the non-linear nature of these models, they provide a better reconstruction performance than optimal linear methods like extended POD (Borée 2003). Finally, we show the potential of deep reinforcement learning in discovering new control strategies for turbulent flows. By framing the fluid-dynamics problem as a multi-agent reinforcement-learning environment and by training the agents using a location-invariant deep deterministic policy-gradient (DDPG) algorithm, we are able to learn a control strategy that achieves a remarkable 30% drag reduction, improving over existing strategies by about 10 percentage points.I den förinställda avhandlingen undersöks tillämpningen av djupinlärning och djupförstärkningsinlärning på turbulenta flödessimuleringar. Modeller för djupinlärning tränas för att utföra tids- och rumsförutsägelser, medan djupförstärkningsinlärning tillämpas på ett flödeskontrollproblem, nämligen minskningen av motståndet i ett öppet kanalflöde. Long short-term memory (LSTM, Hochreiter & Schmidhuber 1997) nätverk och Koopman non-linear forcing (KNF) modeller optimeras för att utföratidsförutsägelser i två turbulensmodeller med reducerad ordning, nämligen nio-ekvationsmodellen som föreslagits av Moehlis et al. (2004) och en trunkerad proper orthogonal decomposition (POD) av ett minimalt kanalflöde (Jiménez & Moin 1991). I den första applikationen kan båda modellerna producera korrekta korttidsförutsägelser, dessutom är de förutsagda trajektorierna statistiskt korrekta. KNF-modeller överträffar LSTM-nätverk i kortsiktiga förutsägelser, med en mycket lägre utbildningsberäkningskostnad. I den andra uppgiften kan endast LSTM nätverken tränas framgångsrikt, med trajektorier som är statistiskt korrekta. Spatiala förutsägelser utförs i två turbulenta flöden, en öppen kanal flöde och en gränsskikt. Fully-convolutional networks (FCN) används för att förutsäga tvådimensionella hastighetsfluktuationsfält vid givet avstånd från väggen med hjälp av väggmätningar (och vice versa). Tack vare deras icke-linjär karaktär ger dessa modeller bättre rekonstruktionsprestanda än optimala linjära metoder som extended POD (Borée 2003). Slutligen visar vi potentialen med djup förstärkningsinlärning för att upptäcka nya kontrollstrategier i turbulenta flöden. Genom att inrama strömningsmekaniska problemet som en förstärknings-inlärningsmiljö med flera agenter och genom att träna agenterna med hjälp av en positionsinvariant deep deterministic policy gradient (DDPG) algoritm, kan vi lära oss en kontrollstrategi som uppnår en anmärkningsvärd 30% minskning av luftmotståndet, vilket jämfört med existerande strategier är en förbättring med cirka 10 procentenheter.QC 230516</p
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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