4 Research products, page 1 of 1

  • 2017-2021
  • Open Access
  • Part of book or chapter of book
  • CH
  • Hyper Article en Ligne
  • Digital Humanities and Cultural Heritage

  • Publication . Other literature type . Preprint . Article . Part of book or chapter of book . 2017
    Open Access
    David Chiavacci; Sébastien Lechevalier;
    Publisher: Informa UK Limited
    Countries: Switzerland, France, France, France, France
    Project: EC | INCAS (645763), EC | INCAS (645763)

    This article has been published as the introduction of the special issue in Japan Forum (“Japanese Political Economy Revisited: Abenomics and Institutional Change”) and as an introduction to the book (Japanese Political Economy Revisited: Abenomics and Institutional Change, Routledge); This introductory article to the special issue on “Japanese Political Economy Revisited: Diverse Corporate Change, Institutional Transformation, and Abenomics” starts with a short summaryof the changing perceptions of Japan’s political economy from its meteoric rise as worldwide leading model in the 1970s and 1980s to its demotiontoa problem and reform case since the later 1990s. Based on this overview, it identifies some striking issue and open questions in this conventional view of Japan’s political economy as problem and the high expectations on Abenomics as Japan’s current economic reform programme. Then we discuss the articles of the special issue and their new contributionsto a better understanding of the developments at the corporate level as well as institutional change and economic reforms at the macro level in the last two decades. Finally, this introductory article ends with a short outlineof a new research programme and four central research questions about the Japanese political economy.

  • Publication . Part of book or chapter of book . Conference object . 2019
    Open Access English
    Elisa Nury;
    Country: Switzerland

    International audience; This paper describes the workflow of the Grammateus project, from gathering data on Greek documentary papyri to the creation of a web application. The first stage is the selection of a corpus and the choice of metadata to record: papyrology specialists gather data from printed editions, existing online resources and digital facsimiles. In the next step, this data is transformed into the EpiDoc standard of XML TEI encoding, to facilitate its reuse by others, and processed for HTML display. We also reuse existing text transcriptions available on . Since these transcriptions may be regularly updated by the scholarly community, we aim to access them dynamically. Although the transcriptions follow the EpiDoc guidelines, the wide diversity of the papyri as well as small inconsistencies in encoding make data reuse challenging. Currently, our data is available on an institutional GitLab repository, and we will archive our final dataset according to the FAIR principles.

  • Publication . Preprint . Other literature type . Part of book or chapter of book . Article . Conference object . 2020
    Open Access English
    Diego Marcos; Ruth Fong; Sylvain Lobry; Rémi Flamary; Nicolas Courty; Devis Tuia;
    Publisher: HAL CCSD
    Countries: Switzerland, Netherlands, France
    Project: ANR | 3IA@cote d'azur (ANR-19-P3IA-0002), ANR | OATMIL (ANR-17-CE23-0012)

    International audience; Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpretable attributes that are present in the scene. We call such an intermediate layer a \emph{semantic bottleneck}. Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision. In this paper, we look into semantic bottlenecks that capture context: we want attributes to be in groups of a few meaningful elements and participate jointly to the final decision. We use a two-layer semantic bottleneck that gathers attributes into interpretable, sparse groups, allowing them contribute differently to the final output depending on the context. We test our contextual semantic interpretable bottleneck (CSIB) on the task of landscape scenicness estimation and train the semantic interpretable bottleneck using an auxiliary database (SUN Attributes). Our model yields in predictions as accurate as a non-interpretable baseline when applied to a real-world test set of Flickr images, all while providing clear and interpretable explanations for each prediction.

  • Open Access English
    Xiucheng Yang; Mathieu Koehl; Pierre Grussenmeyer;
    Publisher: HAL CCSD
    Country: France
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