“One Country, Two Systems,” Three Law Families, and Four Legal Regions: The Emerging Inter-Regional Conflicts of Law in China

With accumulation of sovereign debt in many large OECD countries it seems that attention is heightened on how to manage public resources more effectively. High levels of sovereign debt are partly related to the aftermath of the latest financial crisis, where resolution for many big economies was to intervene and use public resources to put an end to the expansion of the crisis. Public real estate is one of those resources, which’s efficient management has high importance on general public sector efficacy. It seems that governments around the world have a way to go toward efficiency in public real estate management. There seem to be rather wide differences in management practices and quality. This thesis is an attempt to quantify some choices Estonian government could take in terms of its public real estate management. Four different scenarios are compared and Monte Carlo Simulation tool is used for that purpose. Two of the scenarios are related to private sector involvement and two are not. Privatization of public assets does not only mean cashing out for the government. It has wider consequences by introducing market forces where they weren’t before. One of the most important points of interest in this thesis is what effect can market forces and change in incentives have on public real estate management. There can be both, positive and negative effects, but which ones would prevail? The model built during the process of the thesis tries to measure those effects with aggregate net present value and its volatility by looking at 30 years ahead. Simulation analyses is used to vary input variables in the range that seems to be supported by the observations made in the literature and in some cases, where data is not available, also according to more subjective view that of the author’s. As input and their characteristics are different for scenarios, it is of interest to document how do the main outputs, mean NPV and its volatility, vary along with inputs

Mastering Strategy Card Game (Hearthstone) with Improved Techniques

Strategy card game is a well-known genre that is demanding on the intelligent game-play and can be an ideal test-bench for AI. Previous work combines an end-to-end policy function and an optimistic smooth fictitious play, which shows promising performances on the strategy card game Legend of Code and Magic. In this work, we apply such algorithms to Hearthstone, a famous commercial game that is more complicated in game rules and mechanisms. We further propose several improved techniques and consequently achieve significant progress. For a machine-vs-human test we invite a Hearthstone streamer whose best rank was top 10 of the official league in China region that is estimated to be of millions of players. Our models defeat the human player in all Best-of-5 tournaments of full games (including both deck building and battle), showing a strong capability of decision making.Comment: cog2023 ful

Fault classification in dynamic processes using multiclass relevance vector machine and slow feature analysis

This paper proposes a modifed relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39%, while the proposed algorithm has an overall classification rate of 76.1%. This shows the efficiency and advantages of the proposed method

Sox10+ adult stem cells contribute to biomaterial encapsulation and microvascularization.

Implanted biomaterials and biomedical devices generally induce foreign body reaction and end up with encapsulation by a dense avascular fibrous layer enriched in extracellular matrix. Fibroblasts/myofibroblasts are thought to be the major cell type involved in encapsulation, but it is unclear whether and how stem cells contribute to this process. Here we show, for the first time, that Sox10+ adult stem cells contribute to both encapsulation and microvessel formation. Sox10+ adult stem cells were found sparsely in the stroma of subcutaneous loose connective tissues. Upon subcutaneous biomaterial implantation, Sox10+ stem cells were activated and recruited to the biomaterial scaffold, and differentiated into fibroblasts and then myofibroblasts. This differentiation process from Sox10+ stem cells to myofibroblasts could be recapitulated in vitro. On the other hand, Sox10+ stem cells could differentiate into perivascular cells to stabilize newly formed microvessels. Sox10+ stem cells and endothelial cells in three-dimensional co-culture self-assembled into microvessels, and platelet-derived growth factor had chemotactic effect on Sox10+ stem cells. Transplanted Sox10+ stem cells differentiated into smooth muscle cells to stabilize functional microvessels. These findings demonstrate the critical role of adult stem cells in tissue remodeling and unravel the complexity of stem cell fate determination

Air-to-Ground Channel Characterization for Low-Height UAVs in Realistic Network Deployments

Due to the decrease in cost, size and weight, \acp{UAV} are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to \acp{UAV} both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height \acp{UAV} based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios.Comment: 15 pages, accepted in IEEE Transactions on Antennas and Propagatio

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels

Semi-supervised node classification, as a fundamental problem in graph learning, leverages unlabeled nodes along with a small portion of labeled nodes for training. Existing methods rely heavily on high-quality labels, which, however, are expensive to obtain in real-world applications since certain noises are inevitably involved during the labeling process. It hence poses an unavoidable challenge for the learning algorithm to generalize well. In this paper, we propose a novel robust learning objective dubbed pairwise interactions (PI) for the model, such as Graph Neural Network (GNN) to combat noisy labels. Unlike classic robust training approaches that operate on the pointwise interactions between node and class label pairs, PI explicitly forces the embeddings for node pairs that hold a positive PI label to be close to each other, which can be applied to both labeled and unlabeled nodes. We design several instantiations for PI labels based on the graph structure and the node class labels, and further propose a new uncertainty-aware training technique to mitigate the negative effect of the sub-optimal PI labels. Extensive experiments on different datasets and GNN architectures demonstrate the effectiveness of PI, yielding a promising improvement over the state-of-the-art methods.Comment: 16 pages, 3 figure