The flux of secondary anti-deuterons and antihelium produced in the interstellar medium

Several measurements were performed to find antiprotons in the primary cosmic radiation. Because it is difficult to get completely separated secondarily produced antiprotons from primary ones, calculations based on accelerator results were performed for the flux of secondarily produced anti-deuterons and antihelium

Discovery of data dependencies in relational databases

Knowledge discovery in databases is not only the nontrivial extraction of implicit, previously unknown and potentially useful information from databases. We argue that in contrast to machine learning, knowledge discovery in databases should be applied to real world databases. Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases only can be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use, for example, standard set oriented interfaces of relational database management systems in order to apply methods of knowledge discovery in databases. We present a system for discovering data dependencies, which is build upon a set oriented interface. The point of main effort has been put on the discovery of value restrictions, unary inclusion- and functional dependencies in relational databases. The system also embodies an inference relation to minimize database access

Combining statistical learning with a knowledge-based approach

The paper describes a case study in combining different methods for acquiring medical knowledge. Given a huge amount of noisy, high dimensional numerical time series data describing patients in intensive care, the support vector machine is used to learn when and how to change the dose of which drug. Given medical knowledge about and expertise in clinical decision making, a first-order logic knowledge base about effects of therapeutical interventions has been built. As a preprocessing mechanism it uses another statistical method. The integration of numerical and knowledge-based procedures eases the task of validation in two ways. On one hand, the knowledge base is validated with respect to past patients' records. On the other hand, medical interventions that are recommended by learning results are justified by the knowledge base

Over-expression of ST3Gal-I promotes mammary tumorigenesis

Changes in glycosylation are common in malignancy, and as almost all surface proteins are glycosylated, this can dramatically affect the behavior of tumor cells. In breast carcinomas, the O-linked glycans are frequently truncated, often as a result of premature sialylation. The sialyltransferase ST3Gal-I adds sialic acid to the galactose residue of core 1 (Galβ1,3GalNAc) O-glycans and this enzyme is over-expressed in breast cancer resulting in the expression of sialylated core 1 glycans. In order to study the role of ST3Gal-I in mammary tumor development, we developed transgenic mice that over-express the sialyltransferase under the control of the human membrane-bound mucin 1 promoter. These mice were then crossed with PyMT mice that spontaneously develop mammary tumors. As expected, ST3Gal-I transgenic mice showed increased activity and expression of the enzyme in the pregnant and lactating mammary glands, the stomach, lungs and intestine. Although no obvious defects were observed in the fully developed mammary gland, when these mice were crossed with PyMT mice, a highly significant decrease in tumor latency was observed compared to the PyMT mice on an identical background. These results indicate that ST3Gal-I is acting as a tumor promoter in this model of breast cancer. This, we believe, is the first demonstration that over-expression of a glycosyltransferase involved in mucin-type O-linked glycosylation can promote tumorigenesis

A new challenge: approaching Tetris Link with AI

Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper introduces a board game, Tetris Link, that is yet unexplored and appears to be highly challenging. Tetris Link has a large branching factor and lines of play that can be very deceptive, that search has a hard time uncovering. Finding good moves is very difficult for a computer player, our experiments show. We explore heuristic planning and two other approaches: Reinforcement Learning and Monte Carlo tree search. Curiously, a naive heuristic approach that is fueled by expert knowledge is still stronger than the planning and learning approaches. We, therefore, presume that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon.LIACS-Managemen

Procedural content generation: better benchmarks for transfer reinforcement learning

The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. This promises quicker learning and learning more complex methods. To gain an insight into the field and to detect emerging trends, we performed a database search. We note a surprisingly late adoption of deep learning that starts in 2018. The introduction of deep learning has not yet solved the greatest challenge of TRL: generalization. Transfer between different domains works well when domains have strong similarities (e.g. MountainCar to Cartpole), and most TRL publications focus on different tasks within the same domain that have few differences. Most TRL applications we encountered compare their improvements against self-defined baselines, and the field is still missing unified benchmarks. We consider this to be a disappointing situation. For the future, we note that: (1) A clear measure of task similarity is needed. (2) Generalization needs to improve. Promising approaches merge deep learning with planning via MCTS or introduce memory through LSTMs. (3) The lack of benchmarking tools will be remedied to enable meaningful comparison and measure progress. Already Alchemy and Meta-World are emerging as interesting benchmark suites. We note that another development, the increase in procedural content generation (PCG), can improve both benchmarking and generalization in TRL.LIACS-Managemen

Believable Minecraft Settlements by Means of Decentralised Iterative Planning

Procedural city generation that focuses on believability and adaptability to random terrain is a difficult challenge in the field of Procedural Content Generation (PCG). Dozens of researchers compete for a realistic approach in challenges such as the Generative Settlement Design in Minecraft (GDMC), in which our method has won the 2022 competition. This was achieved through a decentralised, iterative planning process that is transferable to similar generation processes that aims to produce "organic" content procedurally.Comment: 8 pages, 8 figures, to be published in "2023 IEEE Conference on Games (CoG)