Creative Cosmologies in Late Gothic Bohemia: Illuminated Diagrams and Memory Tools for the Court of Wenceslas IV

Manuscripta ◽  
2010 ◽  
Vol 54 (1) ◽  
pp. 21-48 ◽  
Author(s):  
Eric Ramírez-Weaver
Keyword(s):  
2017 ◽  
Vol 13 (1) ◽  
pp. 24-38
Author(s):  
Taru Salmenkari

Social movements use memories not only to inspire and mobilize movements but also to struggle for justice. Using memory tools, Taiwanese social movements have challenged official interpretations of history, pluralized subjects worth having their own history and democratized the process of demanding that certain memories should be preserved. They have used memory to fight for social justice and for Taiwanese traditions against modernization and globalization. Social movements have used various memory tool kits, depending on their causes, understandings of Taiwanese identity, current social struggles and access to the political process. Different memory tool kits have led social movements to interpret differently which injustices matter and which gaps in hegemonic narratives deserve their attention.


Author(s):  
Miguel A. Jiménez-Crespo

Translation Memory tools have been widely promoted in terms of increased productivity, quality and consistency, while translation scholars have ar-gued that in some cases they might produce the opposite effect. This paper investigates these two related claims through a corpus-based contrastive analysis of 40,000 original and localized Web pages in Spanish. Given that all Web texts are localized using TM tools, the claim of increased quality and consistency is analyzed in contrast with Web texts spontaneously pro-duced in Spanish. The results of the contrastive analysis indicate that local-ized texts tend to replicate source text structures and show higher numbers of inconsistencies at the lexical, syntactic and typographic levels than non-translated Web sites. These findings are associated with lower levels of quality in localized texts as compared to non-translated or spontaneously produced texts.


Antiquity ◽  
2000 ◽  
Vol 74 (285) ◽  
pp. 475-476 ◽  
Author(s):  
Sarah Kielt Costello
Keyword(s):  

2017 ◽  
Author(s):  
Francesco Versaci ◽  
Luca Pireddu ◽  
Gianluigi Zanetti

AbstractModern sequencing machines produce order of a terabyte of data per day, which need subsequently to go through a complex processing pipeline. The standard workflow begins with a few independent, shared-memory tools, which communicate by means of intermediate files. Given the constant increase of the amount of data produced, this approach is proving more and more unmanageable, due to its lack of robustness and scalability.In this work we propose the adoption of stream computing to simplify the genomic pipeline, boost its performance and improve its fault-tolerance. We decompose the first steps of the genomic processing in two distinct and specialized modules (preprocessing and alignment) and we loosely compose them via communication through Kafka streams, in order to allow for easy composability and integration in the already existing Hadoop-based pipelines. The proposed solution is then experimentally validated on real data and shown to scale almost linearly.


Author(s):  
Megan Brenneman

This chapter discusses the affordances and constraints for the visual modes of meaning making at the Gettysburg Museum of the American Civil War. The past is remembered in terms of available memory tools, which effectively shape an understanding of history when carefully presented with context to an audience. Visual imagery at the museum presents material in ways that other modes cannot; however, it is dependent on other modes to set proper context during the audience's meaning making process. The museum at Gettysburg relies heavily upon visual modes to compose Civil War histories. The multimodalities (visuals, objects, texts) work synchronously as fragmented pieces of history to create a more whole understanding for the audience.


Memory ◽  
1994 ◽  
Vol 2 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Susan E. Gathercole ◽  
Rosaleen A. McCarthy
Keyword(s):  

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