Monte Carlo Tree Search Algorithm for the Euclidean Steiner Tree Problem, Journal of Telecommunications and Information Technology, 2017, nr 4

This study is concerned with a novel Monte Carlo Tree Search algorithm for the problem of minimal Euclidean Steiner tree on a plane. Given p p p points (terminals) on a plane, the goal is to find a connection between all the points, so that the total sum of the lengths of edges is as low as possible, while an addition of extra points (Steiner points) is allowed. Finding the minimum Steiner tree is known to be np-hard. While exact algorithms exist for this problem in 2D, their efficiency decreases when the number of terminals grows. A novel algorithm based on Upper Confidence Bound for Trees is proposed. It is adapted to the specific characteristics of Steiner trees. A simple heuristic for fast generation of feasible solutions based on Fermat points is proposed together with a correction procedure. By combing Monte Carlo Tree Search and the proposed heuristics, the proposed algorithm is shown to work better than both the greedy heuristic and pure Monte Carlo simulations. Results of numerical experiments for randomly generated and benchmark library problems (from OR-Lib) are presented and discussed

Kohonen Network-Based Adaptation of Non Sequential Data for Use in Convolutional Neural Networks

Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented

The use of urban indicators in forecasting a real estate value with the use of deep neural network

Records of municipal planning documents directly affect the land use. In this way, the market price of the land is also shaped. Awareness of the economic and social consequences of adapting specific solutions is the primary argument that should condition the local policy in terms of spatial planning. The research results indicate that the network trained with attributes which do not describe a property value by its price was able to estimate it with acceptable and satisfactory results. The possibility to use artificial multilayer networks in spatial policy decision-making seems well founded. The research results show the relevance of the assumption that using them for modeling can be helpful in selecting the most advantageous variant of planning arrangements in a local law document which determines the land use and development, therefore impacts its value

Improving tumor targeting and therapeutic potential of Salmonella VNP20009 by displaying cell surface CEA-specific antibodies

Genetically modified Salmonella typhimurium VNP20009 (VNP) is a useful vehicle for cancer therapy and vaccine development but exhibits limited tumor targeting in vivo. We engineered a novel VNP derivative that expressed carcinoembryonic antigen (CEA)-specific single chain antibody fragments (scFv) on the cell surface to increase tumor-specific targeting. There was significant scFv cell surface display visualized by flow cytometry and confocal microscopy when cells were probed with fluorescently labeled CEA. Atomic force microscopy (AFM) measurements on whole bacteria confirmed binding of unlabelled CEA to the displayed scFv. The modified VNP strain exhibited increased localization in the upper gastrointestinal tract of CEA transgenic mice and accumulated in CEA-expressing tumors. Furthermore, treatment with a single dose of the VNP derivative inhibited growth of MC38CEA tumors and was associated with local accumulation of CD3(+) T cells and CD11b(+) macrophages. The display of antibody fragments on the surface of VNP represents a novel strategy for both targeting CEA-expressing tumors and increasing the immunogenicity of Salmonella-based vaccines for cancer