By computing anomalous dimensions of gauge invariant composite operators (ψ̄ψ) and (φφ) in Chern-Simons fermion and boson models, we address that Chern-Simons interactions make these operators more relevant or less irrelevant in the low energy region. We obtain a critical Chern-Simons fermion coupling, 1 κ2c = 6 19 , for a phase transition at which the leading irrelevant four-fermion operator (ψ̄...

With growing amounts of text data the descriptive metadata become more crucial in efficient processing of it. One kind of such metadata are keywords, which we can encounter e.g. in everyday browsing of webpages. Such metadata can be of benefit in various scenarios, such as web search or contentbased recommendation. We research keyword extraction problem from the perspective of vector space and ...

Friedrich Hayek wrote frequently on the consequences of trade unions, usually highly critically, making large claims about their very adverse economic and social impact. A close analysis of his work demonstrates that his judgments do not rest on a theory of trade unions which is clearly different from a conventional treatment; nor does he anywhere present any relevant new empirical work. Furthe...

Molecular profiling technologies monitor thousands of transcripts, proteins, metabolites or other species concurrently in biological samples of interest. Given two-class, high-dimensional profiling data, nominal Liknon [4] is a specific implementation of a methodology for performing simultaneous relevant feature identification and classification. It exploits the well-known property that minimiz...

This paper presents a methodology for steganalysis based on a set of 193 features with two main goals. The first goal is to determine a sufficient number of images for effective training of a classifier in the obtained high-dimensional space. Second goal is to use feature selection to select most relevant features for the desired classification. Dimensionality reduction is performed using a for...

Feature selection is one of the most prominent learning tasks, especially in highdimensional datasets in which the goal is to understand the mechanisms that underly the learning dataset. However most of them typically deliver just a flat set of relevant features and provide no further information on what kind of structures, e.g. feature groupings, might underly the set of relevant features. In ...

In order to compare the policies more efficiently, we introduce a new reinforcement learning method called concurrent biased learning. This is a multi-thread learning method, in which each learning thread refers to one feature of the environment. If an agent intentionally focuses on part of these environmental features to learn a policy of a task, we call this method a biased learning; otherwis...

With the increased amount of network threats and intrusions, finding an efficient and reliable defence measure has a great focus as a research field. Intrusion detection systems (IDSs) have been widely deployed as effective defence measure for existing networks. IDSs detect anomalies based on features extracted from network traffic. Network traffic has many features to measure. The problem is t...

Many machine learning methods can produce variable importance scores expressing the usability of each feature in context of the produced model; those scores on their own are yet not sufficient to generate feature selection, especially when an all relevant selection is required. Although there are wrapper methods aiming to solve this problem, they introduce a substantial increase in the required...