1,721,006 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
On the sufficiency of K-positivity for truncated compactly supported generalized moment problems
No full textGiven a compact set K and a finite set of continuous basis functions, the truncated generalized K-moment problem asks for a characterization of all sequences that can be obtained as moments, with respect to the basis functions, of some nonnegative measure with support in K. Under a condition that a certain convex cone is nonempty, the moment sequences can be characterized as being the elements in the dual cone of the closure. This dual cone is also known as the set of all sequences for which the corresponding Riesz functional is K-positive. Here, we give a short, alternative proof of this statement, based on convex optimization and duality. We then present two examples. The first example shows that if the nonemptiness condition is removed, then K-positivity is in general no longer a sufficient condition for a sequence to be a moment sequence. Nevertheless, the second example shows that there are moment problems where the convex cone is empty, but for which K-positivity is still a necessary and sufficient condition for the existence of a representing measure.</p
Det cirkulara rationella kovariansutvidgningsproblemet for skev-periodiskaprocesser
No full textThe Rational Covariance Extension Problem is a problemin applied mathematics where one tries to find a rational spectral density thatmatches a finite covariance sequence. Applications of this can be used in areaslike speech- and image-processing. This problem has been studied intensivelyover the last decades and recently a related problem, the Circulant RationalCovariance Extension Problem, was solved. This version of the problem dealswith periodic stochastic sequences, and was shown to be a natural way toapproximate the solution to the original problem. Here we look at the specialcase when the process in question is skew-periodic, and show that also in thiscase a unique solution to the problem exists. Moreover we develop numerical solversfor both the periodic and the skew-periodic problem, and use these algorithms toapproximate the spectrum from a speech signal.Det Rationella Kovariansutvidgningsproblemet är ett problem inom tillämpad matematik där man försöker hitta en rationell spektraltäthet som matchar en given sekvens av kovarianser. Tillämpningar av problemet finns inom områden som tal- och bildbehandling. Problemet har studerats intensivt under de senaste decennierna, och nyligen har ett relaterat problem lösts - nämligen det Cirkulära Rationella Kovariansutvidgningsproblemet. I detta problem arbetar man med periodiska stokastiska processer, och lösningen visade sig vara ett naturligt sätt att approximera lösningen till det första problemet. I denna uppsats tittar vi på specialfallet när processen är skev-periodisk, och visar att det även i detta fall finns en unik lösning. Dessutom utvecklas numeriska lösare för både det periodiska och skev-periodiska problemet, och dessa algoritmer används tillslut för att approximera spektrumet för en talsignal
Multidimensional inverse problems in imaging and identification using low-complexity models, optimal mass transport, and machine learning
No full textThis thesis, which mainly consists of six appended papers, primarily considers a number of inverse problems in imaging and system identification. In particular, the first two papers generalize results for the rational covariance extension problem from one to higher dimensions. The rational covariance extension problem stems from system identification and can be formulated as a trigonometric moment problem, but with a complexity constraint on the sought measure. The papers investigate a solution method based on varia tional regularization and convex optimization. We prove the existence and uniqueness of a solution to the variational problem, both when enforcing exact moment matching and when considering two different versions of approximate moment matching. A number of related questions are also considered, such as well-posedness, and the theory is illustrated with a number of examples. The third paper considers the maximum delay margin problem in robust control: To find the largest time delay in a feedback loop for a linear dynamical system so that there still exists a single controller that stabilizes the system for all delays smaller than or equal to this time delay. A sufficient condition for robust stabilization is recast as an analytic interpolation problem, which leads to an algorithm for computing a lower bound on the maximum delay margin. The algorithm is based on bisection, where positive semi-definiteness of a Pick matrix is used as selection criteria. Paper four investigate the use of optimal transport as a regularizing functional to incorporate prior information in variational formulations for image reconstruction. This is done by observing that the so-called Sinkhorn iterations, which are used to solve large scale optimal transport problems, can be seen as coordinate ascent in a dual optimization problem. Using this, we extend the idea of Sinkhorn iterations and derive a iterative algorithm for computing the proximal operator. This allows us to solve large-scale convex optimization problems that include an optimal transport term. In paper five, optimal transport is used as a loss function in machine learning for inverse problems in imaging. This is motivated by noise in the training data which has a geometrical characteristic. We derive theoretical results that indicate that optimal transport is better at compensating for this type of noise, compared to the standard 2-norm, and the effect is demonstrated in a numerical experiment. The sixth paper considers using machine learning techniques for solving large-scale convex optimization problems. We first parametrizes a family of algorithms, from which a new optimization algorithm is derived. Then we apply machine learning techniques to learn optimal parameters for given families of optimization problems, while imposing a fixed number of iterations in the scheme. By constraining the parameters appropriately, this gives learned optimization algorithms with provable convergence.Denna avhandling, som huvudsakligen består av de sex bifogade artiklarna, berör ett antal olika inversa problem med tillämpning inom bildrekonstruktion och systemidentifiering. The två första artiklarna generaliserar resultat från litteraturen gällande det rationella kovariansutvidgningsproblemet, från det en-dimensionella fallet till det fler-dimensionella fallet. Det rationella kovariansutvidgningsproblemet har sitt ursprung inom systemidentifiering och kan formuleras som ett trigonometriska momentproblem. Momentproblemet är dock av icke-klassisk karaktär, eftersom det sökta måttet har ett bivillkor som begränsar dess komplexitet. Papperna undersöker olika metoder för att lösa problemet, metoder som alla bygger på variationell regularisering och konvex optimering. Vi undersöker både exakt och approximativ kovariansmatchning, och huvudresultaten är bevis av existens och unikhet vad gäller lösning till dessa olika problem. Artiklarna undersöker även ett antal relaterade frågor, så som välställdhet av problemen, och teorin är också illustrerad med ett antal olika exempel och tillämpningar. Det tredje pappret behandlar ett problem inom robust reglering för linjära system: ett systems tidsfördröjningsmarginal. Tidsfördröjningsmarginalen är den längsta tidsfördröjning ett återkopplat linjärt dynamiskt system kan ha så att det fortfarande finns en enda regulator som stabiliserar systemet för alla tidsfördröjningar som är kortare. Artikeln undersöker ett tillräckligt villkor, och formulerar om detta som ett analytiskt interpolationsproblem. Detta leder till en algoritm för att beräkna en undre gräns för tidsfördröjningsmarginalen. Algoritmen bygger på intervallhalveringsmetoden, och använder Pick-matrisens teckenkaraktär som urvalskriterium. Artikel fyra undersöker användandet av optimal masstransport som regulariseringsfunktion vid bildrekonstruktion. Idén är att använda optimal masstransport som ett avstånd mellan bilder, och på så vis kunna inkorporera förhandsinformation i rekonstruktionen. Mer specifikt görs detta genom att utvidga de så kallade Sinkhorn-iterationerna, som används för att beräkna lösningen till optimal masstransportsproblemet. Vi åstadkommer denna utvidgning genom att observera att Sinkhorn-iterationerna är ekvivalent med koordinatvis optimering i ett dualt problem. Med hjälp av detta tar vi fram en algoritm för att beräkna proximal-operatorn till optimal masstransportproblemet, vilket gör att vi kan lösa storskaliga optimeringsproblem som innehåller en sådan term. I femte artikeln använder vi istället optimal masstransport som kostnadsfunktion vid träning av neurala nätverk för att lösa inversa problem inom bildrekonstruktion. Detta motiveras genom tillämpningar där bruset i data är av geometrisk karaktär. Vi presenterar teoretiska resultat som indikerar att optimal masstransport är bättre på att kompensera för denna typ av brus än till exempel 2-normen. Denna effekt demonstreras också i ett numerisk experiment. Det sjätte pappret undersöker användandet av maskininlärning för att lösa storskaliga optimeringsproblem. Detta görs genom att först parametrisera en familj av algoritmer, ur vilken vi också härleder en ny optimeringsmetod. Vi använder sedan maskininlärning för att ta fram optimala parametrar i denna familj av algoritmer, givet en viss familj av optimeringsproblem samt givet att bara ett fixt antal iterationer får göras i lösningsmetoden. Genom att begränsa sökrymden för algoritmparametrarna kan vi också garantera att den inlärda metoden är en konvergent optimeringsalgoritm.QC 20181204</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
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