Innovation systems in motion: an early music case

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (http://dx.doi.org/10.1108/MD-11-2011-0433). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing LimitedPurpose This study aims to provide an in-depth understanding of the innovation system and the learning processes involved in a very specific cultural field: the production of early music. Design/methodology/approach A single case study of the generic value chain in the music production industry describes and analyses the process and the actors involved in editing a new early music collection resulting from the collaboration between a record company and a public research organization. Findings There is a need for new knowledge in the various stages of performance and publication of a new recording. The early music sector is a knowledge-intensive, science-driven sector that can be characterized as a system because the interactions among actors substantially influence final products. Research limitations/implications The single case study represents a specific sector within the music industry. However, its conclusions can be applied to other fields in the cultural heritage sector. Originality/value The literature on innovation in the cultural field primarily focuses on the relationship between art and information and communication technology (ICT). This paper is novel in analysing a case where scientific knowledge is key to new product development, and suggesting that we need to take account of the interactions among cultural heritage entities, universities and other knowledge production organizations. It concludes that these organizations should be involved institutionally in other aspects of the innovation process.Castro-Martínez, E.; Recasens, A.; Jiménez-Sáez, F. (2013). 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Mealiness assessment in apples and peaches using MRI techniques

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Dense prediction tasks typically employ encoder-decoder architectures, but the prevalent convolutions in the decoder are not image-adaptive and can lead to boundary artifacts. Different generalized convolution operations have been introduced to counteract this. We go beyond these by leveraging guidance data to redefine their inherent notion of proximity. Our proposed network layer builds on the permutohedral lattice, which performs sparse convolutions in a high-dimensional space allowing for powerful non-local operations despite small filters. Multiple features with different characteristics span this permutohedral space. In contrast to prior work, we learn these features in a task-specific manner by generalizing the basic permutohedral operations to learnt feature representations. As the resulting objective is complex, a carefully designed framework and learning procedure are introduced, yielding rich feature embeddings in practice. We demonstrate the general applicability of our approach in different joint upsampling tasks. When adding our network layer to state-of-the-art networks for optical flow and semantic segmentation, boundary artifacts are removed and the accuracy is improved.Comment: To appear at GCPR 201

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