1,721,178 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
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
Topics in Generative Modeling of Particle Physics Data
No full textMessungen in Teilchenphysik-Experimenten resultieren in gewaltigen Datenmengen. Das Fes- thalten dieser Daten braucht auf der einen Seite eine enorme Menge an Speicherplatz, und auf der anderen Seite ist eine ähnlich große Menge an Simulationsdaten nötig, um die Messungen zu analysieren. Ausreichend Rechenressourcen für diese beiden Anwendungen bereitzustellen, wird kontinuierlich schwieriger. Diese Arbeit untersucht daher, wie generative Anwendungen von maschinellem Lernen genutzt werden können, um diesem Problem beizukommen.
Wir demonstrieren zuerst, dass ein generatives Modell tatsächlich in der Lage ist, eine größere Anzahl neuer Datenpunkte zu generieren, als benutzt wurden, um das Modell zu trainieren. Dieses Ergebnis stellt den Grundbaustein für die Anwendung von generativen Modellen für schnelle Simulationen dar. Anschließend daran präsentieren wir, wie drei generativen Modelle – ein GAN, ein WGAN und ein BIB-AE – für die schnelle Simulation von Photon-Kaskaden in dem hoch-granularen elektromagnetischen Kalorimeter des geplanten International Large Detectors verwendet werden können. Des weiten zeigen wir, wie die WGAN und BIB-AE Modelle erweit- ert werden könne, um die Simulation von Pion-Kaskaden in einem hadronischen Kalorimeter zu ermöglichen. Sowohl für Photonen, als auch Pionen, zeigen wir, dass die generativen Modelle die Ergebnisse klassischer Simulationsmethoden gut nachahmen können, und dabei signifikant weniger Zeit brauchen, um die Kalorimeter Kaskaden zu simulieren. Darüber hinaus präsentieren wir die Ergebnisse des ersten Trainings eines generativen Modells auf echten Messdaten. Dabei zeigen wir, dass ein so trainiertes Modell eine ähnliche Präzision erreichen kann wie klassische Simulationsmethoden und gleichzeitig wesentlich weniger Simulationszeit und Rechenleistung in Anspruch nimmt. Schlussendlich stellen wir ein online trainiertes generatives Modell vor, welches in der Lage ist Informationen aus Bereichen zu sammeln, die von momentanen Trigger Systemen verworfen werden. Somit könnte ein solches Modell benutzt werden, um den Speicherplatzbedarf moderner Teilchenphysik-Experimente zu reduzieren.The large amount of data collected by current and future particle physics experiments requires both a large amount of space to store the recorded data and a large amount of simulated data to analyze. This presents a significant strain on the available computational resources. This work explores the use of generative machine learning models to address these challenges.
We initially demonstrate the ability of a generative model to generate more data points than it was trained on, thereby showing generative models are a viable approach for fast simulation. Building on this, we demonstrate the use of three generative networks, a GAN, a WGAN, and a BIB-AE with Post Processor, for the fast simulation of photon showers in a highly granular electromagnetic calorimeter, designed for the International Large Detector. We further show how the WGAN and BIB-AE models can be extended to simulate pion showers in a hadronic calorimeter with a high degree of accuracy, while significantly reducing the needed per-shower simulation time. Notably, we also present the first results for a generative model trained on measurement data in particle physics and show that a BIB-AE model, trained on testbeam data, can reach a precision similar to classical simulation tools while providing a significant speedup. Finally, we address the challenge of having limited storage space by presenting a proposal for an online trained generative model. We show that this model can act as a scouting tool for regions currently ignored by trigger setups and be used to extract potential new-physics signals from these regions without requiring additional storage space
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
Development and Performance of a Fast Simulation Tool for Showers in High Granularity Calorimeters based on Deep Generative Models
No full textModern high energy physics experiments fundamentally rely on large quantities of simulateddata, placing significant demands on the available computational resources. Machine learningmethods based on deep generative models promise to reduce the compute time required tosimulate particle showers in the calorimeter system, which constitutes the most computationallyintensive part of a full detector simulation.This work focuses on the development of a first simulation tool based on deep generativemodels for shower simulation in highly granular calorimeters, and subsequently studies itsperformance in a realistic detector geometry. In order to apply these models in a generalsimulation, they must provide a suitable detector response for particles incident under variousangles to, and at various positions in, the detector. Crucially, the physics performance afterreconstruction must remain high, which is the ultimate target of such a simulator.We initially extend the performant Bounded Information Bottleneck Autoencoder (BIB-AE) to simulate showers from photons with varying incident energy and angle to the surface ofthe electromagnetic calorimeter of the International Large Detector (ILD), before studying thesingle particle performance of the model in terms of key calorimetric observables, both beforeand after reconstruction. We then further extend the model to handle an additional angle ofincidence, as well as taking steps to deal with geometry irregularities in order to allow the useof the model at different positions in the calorimeter.As a next step, we describe a generic library that enables the use of generative models withGeant4 and DD4hep, allowing a full integration into standard software ecosystems used inhigh energy physics. We outline the integration of the BIB-AE into this library, allowing afair benchmark of the computational performance of the model. We then simulate showers atdifferent positions with the model, in order to investigate the effects of performing simulationsin an irregular calorimeter geometry.Finally, we study the performance of the BIB-AE when used to simulate photons fromneutral pion decays in the process in terms of key physics observables. We findthat while some deviations from Geant4 occur, they are typically comparable to the MonteCarlo uncertainty, estimated from the performance differences between Geant4 versions
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
- …
