Towards the first machine translation system for Sumerian transliterations

The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture

Towards a linked open data edition of Sumerian corpora

Linguistic Linked Open Data (LLOD) is a flourishing line of research in the language resource community, so far mostly adopted for selected aspects of linguistics, natural language processing and the semantic web, as well as for practical applications in localization and lexicography. Yet, computational philology seems to be somewhat decoupled from the recent progress in this area: even though LOD as a concept is gaining significant popularity in Digital Humanities, existing LLOD standards and vocabularies are not widely used in this community, and philological resources are underrepresented in the LLOD cloud diagram (http://linguistic-lod.org/llod-cloud). In this paper, we present an application of Linguistic Linked Open Data in Assyriology. We describe the LLOD edition of a linguistically annotated corpus of Sumerian, as well as its linking with lexical resources, repositories of annotation terminology, and the museum collections in which the artifacts bearing these texts are kept. The chosen corpus is the Electronic Text Corpus of Sumerian Royal Inscriptions, a well curated and linguistically annotated archive of Sumerian text, in preparation for the creating and linking of other corpora of cuneiform texts, such as the corpus of Ur III administrative and legal Sumerian texts, as part of the Machine Translation and Automated Analysis of Cuneiform Languages project (https://cdli-gh.github.io/mtaac/)

Les compétences au cœur de l’évaluation finale de cours

Pour donner suite à une recommandation de la CEEC, le Cégep de Sainte-Foy a créé un atelier destiné aux professeurs qui leur permet de mettre l’accent sur l’aspect synthèse de l’évaluation finale de leur cours. L’atelier a été conçu pour que les professeurs puissent répondre eux-mêmes aux questions touchant l’élaboration d’une telle évaluation, dont le cœur est l’atteinte des compétences du cours

Machine Translation and Automated Analysis of Cuneiform Languages (MTAAC)

Project Abstract: Ancient Mesopotamia, birthplace of writing, has produced vast numbers of cuneiform tablets that only a handful of highly specialized scholars are able to read. The task of studying them is so labor intensive that the vast majority have not yet been translated, with the result that their contents are not accessible either to historians in other fields or to the wider public. This project will develop and apply new computerised methods to translate and analyse the contents of some 67,000 highly standardised administrative documents from southern Mesopotamia from the 21st century BC. By automating these basic but labor-intensive processes, we will free up scholars’ time. The tools that we will develop, combining machine learning, statistical and neural machine translation technologies, may then be applied to other ancient languages. Similarly, the translations themselves, and the historical, social and economic data extracted from them, will be made publicly available on the web

Investigating the respective impacts of groundwater exploitation and climate change on wetland extension over 150 years

International audiencePeatlands are complex ecosystems driven by many physical, chemical, and biological processes. Peat soils have a significant impact on water quality, ecosystem productivity and greenhouse gas emissions. However, the extent of peatlands is decreasing across the world, mainly because of anthropogenic activities such as drainage for agriculture or groundwater abstractions in underlying aquifers. Potential changes in precipitation and temperature in the future are likely to apply additional pressure to wetland. In this context, a methodology for assessing and comparing the respective impacts of groundwater abstraction and climate change on a groundwater-fed wetland (135 km2) located in Northwest France, is presented. A groundwater model was developed, using flexible boundary conditions to represent surface-subsurface interactions which allowed examination of the extent of the wetland areas. This variable parameter is highly important for land management and is usually not considered in impact studies. The model was coupled with recharge estimation, groundwater abstraction scenarios, and climate change scenarios downscaled from 14 GCMs corresponding to the A1B greenhouse gas (GHG) scenario over the periods 1961-2000 and 2081-2100. Results show that climate change is expected to have an important impact and reduce the surface of wetlands by 5.3-13.6%. In comparison, the impact of groundwater abstraction (100% increase in the expected scenarios) would lead to a maximum decrease of 3.7%. Results also show that the impacts of climate change and groundwater abstraction could be partially mitigated by decreasing or stopping land drainage in specific parts of the area. Water management will require an appropriate compromise which encompasses ecosystem preservation, economic and public domain activities

Composition chimique des dépôts atmosphériques à l’horizon 2020-2040

Les dépôts atmosphériques peuvent causer des dommages importants aux milieux naturels. Des mesures à long terme issues de la surveillance des dépôts atmosphériques en France ont été exploitées pour construire un modèle statistique prévisionnel afin d'estimer les changements de la composition chimique des dépôts atmosphériques à l'horizon 2020-2040. À partir de simulations d'évolutions possibles du climat et des émissions de polluants atmosphériques, les dépôts de sulfate non marin et les dépôts d'ammonium sont susceptibles de diminuer, mais ceux de nitrate pourraient augmenter de façons diverses selon les régions. Le changement des variations saisonnières de pluviométrie pourrait augmenter les flux de dépôts

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO2 emissions will be crucial to prevent further loss of carbon from our soils

Paradoxical low-flow, low-gradient aortic stenosis despite preserved left ventricular ejection fraction : new insights from weights of operatively excised aortic valves

Aims : We reported that patients with small aortic valve area (AVA) and low flow despite preserved left ventricular ejection fraction (LVEF), i.e. ‘paradoxical’ low flow (PLF), have worse outcomes compared with patients with normal flow (NF), although they generally have a lower mean gradient (MG). The aortic valve weight (AVW) excised at the time of valve replacement is a flow-independent marker of stenosis severity. The objective of this study was to compare the AVW of patients with PLF and MG,40 mmHg with the AVW of patients with NF and MG=40 mmHg. Methods and results : We recruited 250 consecutive patients undergoing valve replacement (Cohort A) for severe stenosis. Among them, 33 (13%) were in PLF [LVEF > 50% but stroke volume index (SVi) = 35 mL/m2] with MG 50% and SVi > 35 mL/m2) with MG = 40 mmHg (NF-HG group). Despite a much lower MG (29 ± 7 vs. 53 ± 10 mmHg; P < 0.0001), patients in the PLF-LG group had a similar AVA (0.73 ± 0.12 vs. 0.69 ± 0.13; P = 0.19) compared with those in the NF-HG group. The AVW [median (interquartile): 1.90 (1.63–2.50) vs. 2.60 (1.66–3.32)] and prevalence of bicuspid phenotype (15 vs. 42%) were lower in the PLF-LG group than in the NF-HG group. However, AVWs analysed separately in the tricuspid and bicuspid valves were similar in both groups [tricuspid valves: 1.80 (1.63–2.50) vs. 2.30 (1.58–3.00) g; P = 0.26 and bicuspid valves: 2.72 (1.73–3.61) vs. 2.60 (2.10–3.55) g; P = 0.93]. When using cut-point values of AVW established in another series of non-consecutive patients (n = 150, Cohort B) with NF and concordant Doppler-echocardiographic findings, we found that the percentage of patients with evidence of severe stenosis in Cohort A was 70% in patients with PLF-LG and 86% in patients with NF-HG. Conclusion : The aortic valve weight data reported in this study provide evidence that a large proportion of patients with PLF and low-gradient have a severe stenosis and that the gradient may substantially underestimate stenosis severity in these patients. A multi-parametric approach including all Doppler-echocardiographic parameters of valve function as well as other complementary diagnostic tests may help correctly identify these patients

DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific

DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific