Dissemination of Regenerative Medicine in Japan: Promoting commercialization under the regulatory system

Though Japan has surpassed South Korea in terms of research and development (R&D) in the area of regenerative medicine, South Korea has been more successful at commercialization. This paper focuses on the setup and operation of actual systems that consider the promotion of regenerative medicine in Japan. Analysis of the regulatory systems in Japan and South Korea shows a clear difference between the two countries, although their systems are basically the same. There are two pathways for applying unapproved drugs in clinical research, including regenerative medicine, to human subjects in Japan, whereas there is only one pathway in South Korea, where the Korea Food and Drug Administration (KFDA) is the only authority through which approval can be obtained. Japan has an additional pathway besides approval through the Pharmaceuticals and Medical Devices Agency (PMDA), if the clinical research is conducted within the framework of the Medical Practitioners Law.

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

Efficient exploration strategy is one of essential issues in cooperative multi-agent reinforcement learning (MARL) algorithms requiring complex coordination. In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized execution (CTDE)-based MARL algorithms. The strangeness refers to the degree of unfamiliarity of the observations that an agent visits. In order to give the observation strangeness a global perspective, it is also augmented with the the degree of unfamiliarity of the visited entire state. The exploration bonus is obtained from the strangeness and the proposed exploration method is not much affected by stochastic transitions commonly observed in MARL tasks. To prevent a high exploration bonus from making the MARL training insensitive to extrinsic rewards, we also propose a separate action-value function trained by both extrinsic reward and exploration bonus, on which a behavioral policy to generate transitions is designed based. It makes the CTDE-based MARL algorithms more stable when they are used with an exploration method. Through a comparative evaluation in didactic examples and the StarCraft Multi-Agent Challenge, we show that the proposed exploration method achieves significant performance improvement in the CTDE-based MARL algorithms.Comment: 9 pages, 7 figure

mtCO1-based population structure and genetic diversity of Pacific oyster *Crassostrea gigas* populations acquired from two farms in South Korea

Since the early 1990s in South Korea, climatic and anthropogenic factors have incurred the reduction of the wild seeds of the Pacific oyster, Crassostrea gigas, which raised concerns about losing genetic diversity and accelerating genetic deterioration. We assessed the genetic diversity of C. gigas populations from two farms (Tongyeong and Gadeokdo) on the southern coast, where about 80% of the cultivated oysters in Korea are produced. Tongyeong showed slightly higher diversity than Gadeokdo, but both populations had a similar genetic structure characterized by low nucleotide diversity. Comparative haplotype analyses provided data supporting genetic features of the populations that include (1) weak genotype-locality relationship, (2) low levels of gene flow between populations, and (3) possible seasonal fluctuation of genetic variation within a population. Furthermore, the highly alike haplotype network patterns were observed between the wild and farm populations as well as among the populations in neighboring countries, which suggests that the genetic structure is conserved between wild and hatchery populations, and geographic proximity has minimal influence on the genetic composition