Elaborated subloading surface model for accurate description of cyclic mobility in granular materials

The description of the cyclic mobility observed prior to the liquefaction in geomaterials requires the sophisticated constitutive formulation to describe the plastic deformation induced during the cyclic loading with the small stress amplitude inside the yield surface. This requirement is realized in the subloading surface model, in which the surface enclosing a purely elastic domain is not assumed, while a purely elastic domain is assumed in other elastoplasticity models. The subloading surface model has been applied widely to the monotonic/cyclic loading behaviors of metals, soils, rocks, concrete, etc., and the sufficient predictions have been attained to some extent. The subloading surface model will be elaborated so as to predict also the cyclic mobility accurately in this article. First, the rigorous translation rule of the similarity center of the normal yield and the subloading surfaces, i.e., elastic core, is formulated. Further, the mixed hardening rule in terms of volumetric and deviatoric plastic strain rates and the rotational hardening rule are formulated to describe the induced anisotropy of granular materials. In addition, the material functions for the elastic modulus, the yield function and the isotropic hardening/softening will be modified for the accurate description of the cyclic mobility. Then, the validity of the present formulation will be verified through comparisons with various test data of cyclic mobility.

Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation.

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

Translation in the “contact zone” between accounting and human resource management

Purpose The purpose of this paper is to develop an understanding of the process through which ideas are translated across disciplines. It does this by focussing on how the idea that people are corporate assets was translated between the accounting and human resource management (HRM) disciplines. Design/methodology/approach This paper is based on the interpretation of a historical case study of the travel of ideas between the accounting and HRM disciplines. Translation is used as an analytical lens as opposed to being the object of the study and is theorised drawing on insights from the Scandinavian Institutionalist School, Skopos theory and linguistic translation techniques. Findings Translation by individual translators involved the translator stepping across disciplinary boundaries. However, translation performed by interdisciplinary teams occurs in the “contact zone” between disciplines. In this zone, both disciplines are, at once, source and target. Ideas are translated by editing and fusing them. In both cases, translation is value laden as the motives of the translators determine the translation techniques used. Legitimacy and gravitas of the translator, as well as contextual opportunities, influence the spread of the idea while disciplinary norms limit its ability to become institutionalised. Also, differential application of the same translation rule leads to heterogeneous outcomes. Originality/value This is the first accounting translation study to use the theories of the Scandinavian Institutionalist School or indeed combine these with linguistic translation techniques. It is also the first study in accounting which explores the translation of ideas across disciplines.

Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation

In this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based, Phrase-based, Neural) to gain insights into their strengths and weaknesses in much more detail than provided by current evaluation schemes. Translating between two languages requires substantial modelling of knowledge about the two languages, about translation, and about the world. Using English-German IT-domain translation as a case-study, we also enhance the Phrase-based system by exploiting parallel treebanks for syntax-aware phrase extraction and by interfacing with Linked Open Data (LOD) for extracting named entity translations in a post decoding framework.

Vélþýðingar á íslensku og Apertium-þýðingarkerfið

There has been rapid development in language technology and machine translation in recent decades. There are three main types of machine translation: statistical ma-chine translation, rule-based machine translation, and example-based machine translation. In this article the Apertium machine translation system is discussed in particular. While Apertium was originally designed to translate between closely related languages, it can now handle languages that are much more different and variable in structure. Anyone can participate in the development of the Apertium system since it is an open source soft ware. Thus Apertium is one of the best options available in order to research and develop a machine translation system for Icelandic. The Apertium system has an easy-to-use interface, and it translates almost instantly from Icelandic into English or Swedish. However, the system still has certain limitations as regards vocabulary and ambiguity.