User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages)

Essex ACL - Broadening horizons with blended learning - technology toolkit for foreign language tutors

As part of an LSIS Regional Response Fund project, Essex Adult Community Learning (ACL) has created a toolkit. The toolkit provides training for foreign language tutors in producing digital resources which combine audio, video, text and communication activities. The toolkit which is now an integral part of a blended learning language course, has also developed tutors' skills in using technology for teaching and learning. The main aim has also been to provide an alternative and flexible method of delivery, especially where funding cuts have impacted on the cost of running taught courses.

Media translation and lesser-used languages: implications of subtitles for Irish language broadcastind

Eguíluz, Federico; Merino, Raquel; Olsen, Vickie; Pajares, Eterio; Santamaría, José Miguel (eds.)

Knowledge, attitudes and perceptions of management strategies and regulations of the Florida Keys National Marine Sanctuary by commercial fishers, dive operators, and environmental group members: A baseline characterization and 10-year comparison

This research is part of the Socioeconomic Research & Monitoring Program for the Florida Keys National Marine Sanctuary (FKNMS), which was initiated in 1998. In 1995-96, a baseline study on the knowledge, attitudes and perceptions of proposed FKNMS management strategies and regulations of commercial fishers, dive operators and on selected environmental group members was conducted by researchers at the University of Florida and the University of Miami’s Rosenstiel School of Atmospheric and Marine Science (RSMAS). The baseline study was funded by the U.S. Man and the Biosphere Program, and components of the study were published by Florida Sea Grant and in several peer reviewed journals. The study was accepted into the Socioeconomic Research & Monitoring Program at a workshop to design the program in 1998, and workshop participants recommended that the study be replicated every ten years. The 10-year replication was conducted in 2004-05 (commercial fishers) 2006 (dive operators) and 2007 (environmental group members) by the same researchers at RSMAS, while the University of Florida researchers were replaced by Thomas J. Murray & Associates, Inc., which conducted the commercial fishing panels in the FKNMS. The 10-year replication study was funded by NOAA’s Coral Reef Conservation Program. The study not only makes 10-year comparisons in the knowledge, attitudes and perceptions of FKNMS management strategies and regulations, but it also establishes new baselines for future monitoring efforts. Things change, and following the principles of “adaptive management”, management has responded with changes in the management plan strategies and regulations. Some of the management strategies and regulations that were being proposed at the time of the baseline 1995-96 study were changed before the management plan and regulations went into effect in July 1997. This was especially true for the main focus of the study which was the various types of marine zones in the draft and final zoning action plan. Some of the zones proposed were changed significantly and subsequently new zones have been created. This study includes 10-year comparisons of socioeconomic/demographic profiles of each user group; sources and usefulness of information; knowledge of purposes of FKNMS zones; perceived beneficiaries of the FKNMS zones; views on FKNMS processes to develop management strategies and regulations; views on FKNMS zone outcomes; views on FKNMS performance; and general support for FKNMS. In addition to new baseline information on FKNMS zones, new baseline information was developed for spatial use, investment and costs-and-earnings for commercial fishers and dive operators, and views on resource conditions for all three user groups. Statistical tests were done to detect significant changes in both the distribution of responses to questions and changes in mean scores for items replicated over the 10-year period. (PDF has 143 pages.)

An Open and Social Spaced Repetition System for Language Learning

In the last decades big improvements have been done in the field of computer aided learning, based on improvements done in computer science and computer systems. Although the field has been always a bit lagged, without using the latest solutions, it has constantly gone forward taking profit of the innovations as they show up. As long as the train of the computer science does not stop (and it won’t at least in the near future) the systems that take profit of those improvements will not either, because we humans will always need to study; Sometimes for pleasure and some other many times out of need. Not all the attempts in the field of computer aided learning have been in the same direction. Most of them address one or some few of the problems that show while studying and don’t take into account solutions proposed for some other problems. The reasons for this can be varied. Sometimes the solutions simply are not compatible. Some other times, because the project is an investigation it’s interesting to isolate the problem. And, in commercial products, licenses and patents often prevent the new projects to use previous work. The world moved forward and this is an attempt to use some of the options offered by technology, mixing some old ideas with new ones.

Digging into signs: Developing standard annotation practices for cross-linguistic, quantitative analysis of sign language data. Final Report.

For sign languages used by deaf communities, linguistic corpora have until recently been unavailable, due to the lack of a writing system and a written culture in these communities, and the very recent advent of digital video. Recent improvements in video and computer technology have now made larger sign language datasets possible; however, large sign language datasets that are fully machine-readable are still elusive. This is due to two challenges. 1. Inconsistencies that arise when signs are annotated by means of spoken/written language. 2. The fact that many parts of signed interaction are not necessarily fully composed of lexical signs (equivalent of words), instead consisting of constructions that are less conventionalised. As sign language corpus building progresses, the potential for some standards in annotation is beginning to emerge. But before this project, there were no attempts to standardise these practices across corpora, which is required to be able to compare data crosslinguistically. This project thus had the following aims: 1. To develop annotation standards for glosses (lexical/word level) 2. To test their reliability and validity 3. To improve current software tools that facilitate a reliable workflow Overall the project aimed not only to set a standard for the whole field of sign language studies throughout the world but also to make significant advances toward two of the world’s largest machine-readable datasets for sign languages – specifically the BSL Corpus (British Sign Language, http://bslcorpusproject.org) and the Corpus NGT (Sign Language of the Netherlands, http://www.ru.nl/corpusngt).

The Economics of "Why is it so hard to save a threatened Language?"

We study the language choice behavior of bilingual speakers in modern societies, such as the Basque Country, Ireland andWales. These countries have two o cial languages:A, spoken by all, and B, spoken by a minority. We think of the bilinguals in those societies as a population playing repeatedly a Bayesian game in which, they must choose strategically the language, A or B, that might be used in the interaction. The choice has to be made under imperfect information about the linguistic type of the interlocutors. We take the Nash equilibrium of the language use game as a model for real life language choice behavior. It is shown that the predictions made with this model t very well the data about the actual use, contained in the censuses, of Basque, Irish and Welsh languages. Then the question posed by Fishman (2001),which appears in the title, is answered as follows: it is hard, mainly, because bilingual speakers have reached an equilibrium which is evolutionary stable. This means that to solve fast and in a re ex manner their frequent language coordination problem, bilinguals have developed linguistic conventions based chie y on the strategy 'Use the same language as your interlocutor', which weakens the actual use of B.1