The European Plate Observing System and the Arctic

The European Plate Observing System (EPOS) aims to integrate existing infrastructures in the solid earth sciences into a single infrastructure, enabling earth scientists across Europe to combine, model, and interpret multidisciplinary datasets at different time and length scales. In particular, a primary objective is to integrate existing research infrastructures within the fields of seismology, geodesy, geophysics, geology, rock physics, and volcanology at a pan-European level. The added value of such integration is not visible through individual analyses of data from each research infrastructure; it needs to be understood in a long-term perspective that includes the time when changes implied by current scientific research results are fully realized and their societal impacts have become clear. EPOS is now entering its implementation phase following a four-year preparatory phase during which 18 member countries in Europe contributed more than 250 research infrastructures to the building of this pan-European vision. The Arctic covers a significant portion of the European plate and therefore plays an important part in research on the solid earth in Europe. However, the work environment in the Arctic is challenging. First, most of the European Plate boundary in the Arctic is offshore, and hence, sub-sea networks must be built for solid earth observation. Second, ice covers the Arctic Ocean where the European Plate boundary crosses through the Gakkel Ridge, so innovative technologies are needed to monitor solid earth deformation. Therefore, research collaboration with other disciplines such as physical oceanography, marine acoustics, and geo-biology is necessary. The establishment of efficient research infrastructures suitable for these challenging conditions is essential both to reduce costs and to stimulate multidisciplinary research.Le système European Plate Observing System (EPOS) vise l’intégration des infrastructures actuelles en sciences de la croûte terrestre afin de ne former qu’une seule infrastructure pour que les spécialistes des sciences de la Terre des quatre coins de l’Europe puissent combiner, modéliser et interpréter des ensembles de données multidisciplinaires moyennant diverses échelles de temps et de longueur. Un des principaux objectifs consiste plus particulièrement à intégrer les infrastructures de recherche existantes se rapportant aux domaines de la sismologie, de la géodésie, de la géophysique, de la géologie, de la physique des roches et de la volcanologie à l’échelle paneuropéenne. La valeur ajoutée de cette intégration n’est pas visible au moyen des analyses individuelles des données émanant de chaque infrastructure de recherche. Elle doit plutôt être considérée à la lumière d’une perspective à long terme, lorsque les changements qu’impliquent les résultats de recherche scientifique actuels auront été entièrement réalisés et que les incidences sur la société seront claires. Le système EPOS est en train d’amorcer sa phase de mise en oeuvre. Cette phase succède à la phase préparatoire de quatre ans pendant laquelle 18 pays membres de l’Europe ont soumis plus de 250 infrastructures de recherche en vue de l’édification de cette vision paneuropéenne. L’Arctique couvre une grande partie de la plaque européenne et par conséquent, il joue un rôle important dans les travaux de recherche portant sur la croûte terrestre en Europe. Cependant, le milieu de travail de l’Arctique n’est pas sans défis. Premièrement, la majorité de la limite de la plaque européenne se trouvant dans l’Arctique est située au large, ce qui signifie que des réseaux marins doivent être aménagés pour permettre l’observation de la croûte terrestre. Deuxièmement, de la glace recouvre l’océan Arctique, là où la limite de la plaque européenne traverse la dorsale de Gakkel, ce qui signifie qu’il faut recourir à des technologies innovatrices pour surveiller la déformation de la croûte terrestre. C’est pourquoi les travaux de recherche doivent nécessairement se faire en collaboration avec d’autres disciplines comme l’océanographie physique, l’acoustique marine et la géobiologie. L’établissement d’infrastructures de recherche efficaces capables de faire face à ces conditions rigoureuses s’avère essentiel, tant pour réduire les coûts que pour stimuler la recherche multidisciplinaire

Unsupervised Clustering of Structured and Unstructured Text Collections

This thesis explores the application of unsupervised clustering for domain adaptation of machine translation systems. As in many artificial intelligence areas, creating a system that generalizes to any domain is a hard problem in machine translation. Domain adaptation, in contrast, aims to specialize a generic (or otherwise intended) system for a particular domain and translate text within that domain better. In this thesis, experiments on using unsupervised learning as a first step in solving this problem are explored, posing the research questions a) how unstructured data could be used for domain adaptation and b) how a bespoke translation of an input document could be provided. In the first part of the thesis, background theory is presented and related work reviewed. In the second, experimental part, preliminary experiments on building n-gram models and multiword expression detection are presented before experiments into clustering of structured and unstructured document collections are conducted. Finally, the parts are brought together in experiments on using these input factors for domain adaptation of machine translation systems, with end-to-end evaluation. Some of the clusters identified in the clustering experiments on unstructured web collections were used as auxiliary language models in machine translation, in the experiments on domain adaptation. Self-Organizing Maps are used in the first phase of unsupervised clustering before a hierarchical agglomerative clustering algorithm is applied to extract tangible clusters from the map, with the number of clusters determined by the knee method. By creating a mapping between the input document and one of the auxiliary language models, translation is aided by this language model. Using the language model perplexity on the input documents to select the auxiliary language model for domain adaptation links the clusters to the translation process. Results show that the performance according to metrics such as BLEU, TER, and Meteor were on-par, and in some cases better than the results from leveraging all the available supplementary text as an auxiliary language model. The difference when using different auxiliary LM could be up to 1 BLEU points and 0.9 Meteor points

Automatisk oversettelse av norske substantivkomposita : En eksperimentell studie

I denne oppgaven foretas en diskusjon av komposita (sammensetninger) og substantivkomposita i særdeleshet. På bakgrunn av utfordringene med å oversette slike konstruksjoner og tidligere arbeider, blir en studie gjennomført med sikte på å oversette 750 komposita trukket ut fra løpende norsk tekst til engelsk. Studien ønsket å kaste lys på hvilke utslag korpusstørrelse, rangeringsmetodikk og analysedybde gir på ytelsen. Metodene som blir brukt deler først kompositaene opp i to deler og oversetter disse og setter resultatet sammen til nye fraser på engelsk. Dette gir mange oversettelseskandidater for hvert norske kompositum, og en maskinlæringsteknikk basert på maksimalentropi blir brukt til å rangere disse. Den høyest rangerte kandidaten blir sammenliknet med oversettelser fra en tospråklig informant, oversettelser som også danner grunnlaget for å trene maskinlæringsmotoren. 10-dobbel kryssvalidering blir brukt for at modellene ikke blir brukt til å evaluere oversettelser av komposita de er trent på. Av de 750 tilfeldige kompositaene var bare 4,5% oppført i Kunnskapsforlagets Engelsk Stor Ordbok. Ved hjelp av den beste rangeringsmodellen fra studien ble 50% av ordene oversatt til den foretrukne oversettelsen fra informanten

Modeling spatial and temporal dependencies between earthquakes

Two new different stochastic models for earthquake occurrence are discussed. Both models are focusing on the spatio-temporal interactions between earthquakes. The parameters of the models are estimated from a Bayesian updating of priors, using empirical data to derive posterior distributions. The first model is a marked point process model in which each earthquake is represented by its magnitude and coordinates in space and time. This model incorporates the occurrence of aftershocks as well as the build-up and subsequent release of strain. The second model is a hierarchical Bayesian space-time model in which the earthquakes are represented by potentials on a grid. The final ambition of the models is to make predictions on the occurrence of earthquakes