Export commodity production and broad-based rural development: coffee and cocoa in the Dominican Republic

An estimated 80,000-100,000 Dominican farmers produce coffee and cocoa, nearly 40 percent of all agricultural producers. The sectors also provide employment for tens of thousands of field laborers and persons employed in linked economic activities. The majority of coffee and cocoa producers are small-scale and most are located in environmentally sensitive watersheds. Recent trends in international commodity markets have challenged the survival of both sectors. Production is characterized by low yields and uneven quality, while periodic hurricanes have contributed to a lackluster and unstable record of output and exports. Despite these conditions, most experts acknowledge the fact that appropriate agro-ecological conditions exist in Dominican Republic for production of high-quality coffee and cocoa. To be competitive and sustainable, some changes must take place in the coffee and cocoa sectors. The objective of this study is to provide an overview of the coffee and cocoa sectors, to identify major problems, and to suggest possible strategies to deal with these problems. The authors conclude that if the objectives of the government are poverty reduction, environmental protection and overall well-being of rural society, it is critical to move beyond a commodity-specific approach to a broader rural development focus on households, regions and environments where coffee and cocoa are currently being grown.Environmental Economics&Policies,Banks&Banking Reform,Economic Theory&Research,Crops&Crop Management Systems,Agricultural Knowledge&Information Systems,Crops&Crop Management Systems,Environmental Economics&Policies,Economic Theory&Research,Banks&Banking Reform,Agricultural Knowledge&Information Systems

What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

Measuring analyticity and syntheticity in creoles

Creoles (here including expanded pidgins) are commonly viewed as being more analytic than their lexifiers and other languages in terms of grammatical marking. The purpose of the study reported in this article was to examine the validity of this view by measuring the frequency of analytic (and synthetic) markers in corpora of two different English-lexified creoles - Tok Pisin and Hawai'i Creole- and comparing the quantitative results with those for other language varieties. To measure token frequency, 1,000 randomly selected words in each creole corpus were tagged with regard to word class, and categorized as being analytic, synthetic, both analytic and synthetic, or purely lexical. On this basis, an Analyticity Index and a Syntheticity Index were calculated. These were first compared to indices for other languages and then to L1 varieties of English (e.g. standard British and American English and British dialects) and L2 varieties (e.g. Singapore English and Hong Kong English). Type frequency was determined by the size of the inventories of analytic and synthetic markers used in the corpora, and similar comparisons were made. The results show that in terms of both token and type frequency of grammatical markers, the creoles are not more analytic than the other varieties. However, they are significantly less synthetic, resulting in much higher ratios of analytic to synthetic marking. An explanation for this finding relates to the particular strategy for grammatical expansion used by individuals when the creoles were developing

Creoles and minority dialects in education: An update

This paper renews the call for greater interest in applied work to deal with the obstacles faced in formal education by speakers of creoles (such as Hawai'i Creole and Jamaican Creole) and minority dialects (such as African American English). It starts off with an update on developments in the use of these vernacular languages in educational contexts since 1998, focusing on educational programmes, publications and research by linguists and educators. It goes on to discuss some of the research and public awareness efforts needed to help the speakers of these vernacular varieties, with examples given from Hawai'i