1,720,956 research outputs found
Nederbördsprognoser med Djupa Neurala Nätverk
No full textDeep neural networks (DNNs) based on satellite and radar data have shown promising results for precipitation nowcasting, beating physical models and optical flow for time horizons up to 8 hours. “MetNet”, developed by Google AI, is a 225 million parameter DNN combining three different types of architectures that was trained on satellite and radar data over the United States. They claim to be the first machine learning model to outperform physical models at such a scale. In this work, we implemented a similar but simplified model trained on radar-only Swedish data, with the aim to perform precipitation nowcasting for up to 2 hours into the future. Furthermore, we compare the model to another, simpler model that omits the spatial aggregator of the DNN architecture which is a state-of-the-art vision transformer. Our results show that, although the adopted training dataset was too small to prevent overfitting, the model is still able to outperform the persistence benchmark for lead times longer than 30 minutes with a threshold of 0.2mm/h precipitation. Our simplified model, perhaps unsurprisingly, is outperformed by MetNet because of having too few training data samples or variances in the models’ implementation. We show, nonetheless, that the adopted spatial aggregator fulfills a vital role as expected, aggregating global information into spatial and temporal contexts. Due to the limitations imposed by the reduced size of the model, we cannot, unfortunately, draw definitive conclusions on whether a radar-only model could yield similar forecast skills as MetNet. To improve on these results, more training data is certainly needed. This would require that more robust computation resources are available, but pre-training the model on a larger dataset — or even implementing a model that takes in different geographical locations for training — can naturally lead to significant improvements in the predictions.Djupa neurala nätverk (DNN) baserade på satellit och radar data har gett bra resultat för korta nederbördsprognoser och kan slå fysikaliska modeller och optical flow f ̈or prognoser upp till 8 timmar i framtiden. “MetNet” ̈ar ett 225 million DNN publicerat av Google som kombinerar tre olika typer av djupa arkitekturer, det är tränat på satellit och radar data över USA och är enligt dom den första maskininlärningsmodellen som presterar bättre än fysikaliska modeller. I denna uppsats har vi konstruerat en modell som liknar deras på ett nedskalat problem. Vi har färre parametrar, lägre upplöst data, endast 2 timmar prognostisering och använder bara radar data över Sverige för att träna modellen. Vi använder F1-score för att evaluera modellens prestanda och jämför prognosen mot persistens som referens. Vidare undersöker vi en mindre komplicerad modell där den tredje arkitekturen inte används för att se vilken roll vision transformern har. Våra resultat visar att datasetet vi tränat på är för litet och modellen överanpassas men modellen lyckas ändå slå persistens referensen för prognoser 30–120 minuter när en 0.2mm/h regntröskel tillämpas. Resultaten är sämre än MetNet av Google och vi kan inte dra några slutsatser huruvida en modell med endast radar-data skulle kunna ge liknande resultat eller inte, eftersom modellen inte tränats till dess fulla potential. Vi visar att den tredje arkitekturen, vision transformern, är en viktig del av nätverket och aggregerar global information till lokala kontexter över tid och rum. För att förbättra våra resultat skulle vi pröva att låta modellen träna på det amerikanska datasetet använt av Google och implementera en modell vars input varierar geografisk position
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
No full text“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
No full textWe 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
Elliptic Partial Differential Equations and Spectral Geometry
No full textI denna rapport utforskar vi en del teori kring eliptiska partiella differential ekvationer, i vilka problem de uppstår och metoder för att lösa dem. Mer specifikt försöker vi att sätta oss in i och undersöka frågan ställd av Mark Kac 1966: “Can one hear the shape of a drum?”. Genom att använda metoder från en artikel av C. Gordon, D. Webb and S. Wolpert [GWW92] konstruerar vi två plana domäner som är isospektrala under Laplacianen. Detta ger oss svaret till Kac’s fråga: nej, man kan inte höra formen på en trumma. Med numeriska metoder visualiserar vi några egenmoder för dessa isospektrala domäner och jämför deras egenvärden. Fastän man inte kan höra formen på en trumma ger spektrat en del användbar information. Med Weyl’s Lag kan man beräkna arean, eller till och med omkretsen, av domänen vilket vi diskuterar i sista sektionen.In this paper we explore some theory behind elliptic partial differential equations, in what problems they arise and methods of solving them. Specifically we will try to address the question asked by Mark Kac in 1966: “Can one hear the shape of a drum?”. Using the theory from an article by C. Gordon, D. Webb and S. Wolpert [GWW92] we construct two planar domains which are isospectral under the Laplacian. Thus answering Kac’s question negatively that no, one cannot hear the shape of a drum. With numerical methods we visualize some eigenmodes for these isospectral domains and compare their eigenvalues. Even though one can not hear the shape of a drum the spectrum generate some useful information. With Weyl’s Law one can calculate the area, or even the circumference, of the domain which we discuss in the last section.
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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