Overview of the charging situation for digital contents in Japan: From the viewpoint of compensation for private sound and visual recording

Japanese copyright law consists of two parts. One has to do with the rights of the content holder. The other is concerned with limitations to the rights of the content holder, such as compensation for private sound and visual recording; that is, charges for copying media where it is difficult to charge individually, as is the case with Digital Rights Management (DRM ). Such compensation permits the holders of the content to collect royalties through a special compensation arrangement. That is, the designated management associations impose the obligation for compensation on the manufacturers of recording devices. Despite the spread of such compensation arrangements, new challenges continue to arise, such the case of SARVH, the designated management association, brought against Toshiba, a manufacturer of DVD recorders. The Tokyo District Court ruled, 'Compensation is not required under copyright law, but just that all possible efforts be made.' It remains unclear whether holders of content can receive sufficient royalties or not. An analysis of the latest decision regarding digital content, from the point of copyright law, clarifies the relationship between DRM and compensation for private sound and visual recording. To accommodate stakeholders' requirements, a new regulation or structure for payment of royalties is proposed. --copyright law,DRM (Digital Rights Manegement),compensation,levy

Single-epoch supernova classification with deep convolutional neural networks

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.Comment: 7 pages, published as a workshop paper in ICDCS2017, in June 201