120,084 research outputs found
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
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
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
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
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
[Illustrations pour] l'Oiseau bleu, poëme de M. Maeterlinck, musique de A. Wolff / [reprod. photomec. de] dessins de M. Moreels
No full textAppartient à l’ensemble documentaire : IconMUS1Appartient à l’ensemble documentaire : IconMUSNumAppartient à l’ensemble documentaire : IconMUS0Illustration
Probabilistic, Features-Based Object Recognition
No full textObject recognition is of fundamental importance in computer vision. In a few years, pedestrian detection, car detection, and more generally scene recognition will likely be reliable enough to allow fully-automated car navigation, and the human driver will be relegated to the back seat to sip his coffee.
In this thesis we are interested in recognizing individual objects and categories. In order to reduce the volume of information one has to process, images are characterized by sets of features. These features, also called interest points, are targeted at image locations with high local information content. Various systems for detecting interest points and for describing the local image appearance near these points, have been proposed in the last two decades. We investigate which combinations from this plethora of detectors and descriptors, are most suited for object recognition tasks.
On to the problem of object recognition, we are first interested in recognizing individual objects. In a few years, one can imagine that customers in shops, will take with their cell phone a picture of a product that looks interesting, send it to a remote server with a huge database of individual objects, and get back information about that specific product. We propose a system for individual object recognition, inspired from previous work on coarse-to-fine recognition. All steps of the recognition process are translated into principled probabilistic terms, which allows us to outperform a state-of-the-art commercial system for individual recognition.
Regarding categories, faces are probably the category that has received the most attention in computer vision literature. Here we propose a system to recognize images of the same individual in large databases of images. This can be of high interest when looking for images of a given person over the internet. Our method's advantage is that it works on real-world images, as opposed to the face databases from the literature, collected in laboratories with controlled lighting, pose and background conditions.
Finally, we are interested in recognition of object categories in general. Using support vector machines for the classification task, we propose a features-based kernel that improves recognition performance on object categories.</p
Sarah L. Blum Author Visit - Warrior Nurse: PTSD and Healing
No full textHear Sarah L. Blum, author of Women Under Fire: Abuse in the Military, discuss her newest book, Warrior Nurse: PTSD and Healing followed by a Q&A and book signing.
Sarah L. Blum is a decorated Vietnam veteran who served as an operating room nurse during the intense fighting of 1967. In recognition of her service, she was awarded the Army Commendation Medal.
Sponsored by CWU Veterans Center and CWU Libraries.https://digitalcommons.cwu.edu/libraryevents/1252/thumbnail.jp
Lillian L. Lambert, Author, Speaker, and Entrepreneur
No full textLillian L. Lambert, Author, Speaker, and Entrepreneu
Letter to Alfred L. Shoemaker, February 10, 1948
No full textA handwritten letter from an unknown author addressed to Alfred L. Shoemaker, dated February 10, 1948. Within, the author discusses the Pennsylvania Dutch word for Ash Wednesday, along with traditions associated with this day.https://digitalcommons.ursinus.edu/shoemaker_documents/1118/thumbnail.jp
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