1,720,966 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
Adapting to Variations in Textile Properties for Robotic Manipulation
No full textIn spite of the rapid advancements in AI, tasks like laundry, tidying, and general household assistance remain challenging for robots due to their limited capacity to generalize manipulation skills across different variations of everyday objects.Manipulation of textiles, in particular, poses unique challenges due to their deformable nature and complex dynamics. In this thesis, we aim to enhance the generalization of robotic manipulation skills for textiles by addressing how robots can adapt their strategies based on the physical properties of deformable objects. We begin by identifying key factors of variation in textiles relevant to manipulation, drawing insights from overlooked taxonomies in the textile industry. The core challenge of adaptation is addressed by leveraging the synergies between interactive perception and cloth dynamics models. These are utilized to tackle two fundamental estimation problems to achieve adaptation: property identification, as these properties define the system’s dynamic and how the object responds to external forces, and state estimation, which provides the feedback necessary for closing the action-perception loop. To identify object properties, we investigate how combining exploratory actions, such as pulling and twisting, with sensory feedback can enhance a robot’s understanding of textile characteristics. Central to this investigation is the development of an adaptation module designed to encode textile properties from recent observations, enabling data-driven dynamics models to adjust their predictions accordingly to the perceived properties. To address state estimation challenges arising from cloth self-occlusions, we explore semantic descriptors and 3D tracking methods that integrate geometric observations, such as point clouds, with visual cues from RGB data.Finally, we integrate these modeling and perceptual components into a model-based manipulation framework and evaluate the generalization of the proposed method across a diverse set of real-world textiles. The results, demonstrating enhanced generalization, underscore the potential of adapting the manipulation in response to variations in textiles' properties and highlight the critical role of the action-perception loop in achieving adaptability.Trots de snabba framstegen inom AI förblir uppgifter som att tvätta, städa och allmän hushållshjälp utmanande för robotar på grund av deras begränsade förmåga att generalisera manipulationsfärdigheter över olika variationer av vardagsföremål. Manipulation av textilier utgör i synnerhet unika utmaningar på grund av deras deformerbara natur och komplexa dynamik.I denna avhandling syftar vi till att förbättra generaliseringen av robotiska manipulationsfärdigheter för textilier genom att undersöka hur robotar kan anpassa sina strategier baserat på de fysiska egenskaperna hos deformerbara objekt. Vi börjar med att identifiera nyckelfaktorer för variation i textilier som är relevanta för manipulation och drar insikter från förbisedda taxonomier inom textilindustrin.Den centrala utmaningen med anpassning adresseras genom att utnyttja synergierna mellan interaktiv perception och modeller för textildynamik. Dessa används för att lösa två grundläggande estimeringsproblem för att uppnå anpassning: egenskapsidentifiering, eftersom dessa egenskaper definierar systemets dynamik och hur objektet reagerar på yttre krafter, samt tillståndsestimering, som ger den återkoppling som krävs för att stänga åtgärds-perceptionsslingan. För att identifiera objektets egenskaper undersöker vi hur kombinationen av utforskande handlingar, såsom att dra och vrida, med sensorisk återkoppling kan förbättra robotens förståelse för textilens egenskaper. Centralt i denna undersökning är utvecklingen av en anpassningsmodul utformad för att koda textilens egenskaper från nyligen gjorda observationer, vilket gör det möjligt för datadrivna dynamikmodeller att justera sina förutsägelser utifrån de uppfattade egenskaperna.För att hantera utmaningar med tillståndsestimering som uppstår vid textilens självocklusioner utforskar vi semantiska deskriptorer och 3D-spårningsmetoder som integrerar geometriska observationer, såsom punktmoln, med visuella ledtrådar från RGB-data.Slutligen integrerar vi dessa modellerings- och perceptionskomponenter i ett modellbaserat manipulationsramverk och utvärderar generaliseringen av den föreslagna metoden på ett brett urval av textilier i verkliga miljöer. Resultaten, som visar förbättrad generalisering, understryker potentialen i att anpassa manipulation till variationer i textilernas egenskaper och framhäver den avgörande rollen för åtgärds-perceptionsslingan i att uppnå anpassningsförmåga.QC 20241213</p
Adapting to Variations in Textile Properties for Robotic Manipulation
No full textIn spite of the rapid advancements in AI, tasks like laundry, tidying, and general household assistance remain challenging for robots due to their limited capacity to generalize manipulation skills across different variations of everyday objects.Manipulation of textiles, in particular, poses unique challenges due to their deformable nature and complex dynamics. In this thesis, we aim to enhance the generalization of robotic manipulation skills for textiles by addressing how robots can adapt their strategies based on the physical properties of deformable objects. We begin by identifying key factors of variation in textiles relevant to manipulation, drawing insights from overlooked taxonomies in the textile industry. The core challenge of adaptation is addressed by leveraging the synergies between interactive perception and cloth dynamics models. These are utilized to tackle two fundamental estimation problems to achieve adaptation: property identification, as these properties define the system’s dynamic and how the object responds to external forces, and state estimation, which provides the feedback necessary for closing the action-perception loop. To identify object properties, we investigate how combining exploratory actions, such as pulling and twisting, with sensory feedback can enhance a robot’s understanding of textile characteristics. Central to this investigation is the development of an adaptation module designed to encode textile properties from recent observations, enabling data-driven dynamics models to adjust their predictions accordingly to the perceived properties. To address state estimation challenges arising from cloth self-occlusions, we explore semantic descriptors and 3D tracking methods that integrate geometric observations, such as point clouds, with visual cues from RGB data.Finally, we integrate these modeling and perceptual components into a model-based manipulation framework and evaluate the generalization of the proposed method across a diverse set of real-world textiles. The results, demonstrating enhanced generalization, underscore the potential of adapting the manipulation in response to variations in textiles' properties and highlight the critical role of the action-perception loop in achieving adaptability.Trots de snabba framstegen inom AI förblir uppgifter som att tvätta, städa och allmän hushållshjälp utmanande för robotar på grund av deras begränsade förmåga att generalisera manipulationsfärdigheter över olika variationer av vardagsföremål. Manipulation av textilier utgör i synnerhet unika utmaningar på grund av deras deformerbara natur och komplexa dynamik.I denna avhandling syftar vi till att förbättra generaliseringen av robotiska manipulationsfärdigheter för textilier genom att undersöka hur robotar kan anpassa sina strategier baserat på de fysiska egenskaperna hos deformerbara objekt. Vi börjar med att identifiera nyckelfaktorer för variation i textilier som är relevanta för manipulation och drar insikter från förbisedda taxonomier inom textilindustrin.Den centrala utmaningen med anpassning adresseras genom att utnyttja synergierna mellan interaktiv perception och modeller för textildynamik. Dessa används för att lösa två grundläggande estimeringsproblem för att uppnå anpassning: egenskapsidentifiering, eftersom dessa egenskaper definierar systemets dynamik och hur objektet reagerar på yttre krafter, samt tillståndsestimering, som ger den återkoppling som krävs för att stänga åtgärds-perceptionsslingan. För att identifiera objektets egenskaper undersöker vi hur kombinationen av utforskande handlingar, såsom att dra och vrida, med sensorisk återkoppling kan förbättra robotens förståelse för textilens egenskaper. Centralt i denna undersökning är utvecklingen av en anpassningsmodul utformad för att koda textilens egenskaper från nyligen gjorda observationer, vilket gör det möjligt för datadrivna dynamikmodeller att justera sina förutsägelser utifrån de uppfattade egenskaperna.För att hantera utmaningar med tillståndsestimering som uppstår vid textilens självocklusioner utforskar vi semantiska deskriptorer och 3D-spårningsmetoder som integrerar geometriska observationer, såsom punktmoln, med visuella ledtrådar från RGB-data.Slutligen integrerar vi dessa modellerings- och perceptionskomponenter i ett modellbaserat manipulationsramverk och utvärderar generaliseringen av den föreslagna metoden på ett brett urval av textilier i verkliga miljöer. Resultaten, som visar förbättrad generalisering, understryker potentialen i att anpassa manipulation till variationer i textilernas egenskaper och framhäver den avgörande rollen för åtgärds-perceptionsslingan i att uppnå anpassningsförmåga.QC 20241213</p
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
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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