Scientometrics: Untangling the topics

Measuring science is based on comparing articles to similar others. However, keyword-based groups of thematically similar articles are dominantly small. These small sizes keep the statistical errors of comparisons high. With the growing availability of bibliographic data such statistical errors can be reduced by merging methods of thematic grouping, citation networks and keyword co-usage.Comment: 2 pages, 2 figure

Mezoszkopikus és kvantum kaotikus rendszerek statisztikai és dinamikai vizsgálata = Statistical and dynamical investigation of mesoscopic and quantum chaotic systems

-Sűrűségoperátorok tulajdonságait vizsgáltuk a wavelet analízis segítségével. -Inverz mágneses doméneket illetve hibrid biliárdokat tanulmányoztunk szemiklasszikus analízis alkalmazásával. -Husimi reprezentációban tanulmányoztuk olyan rendszerek viselkedését, melyeknél nem értelmezhető a klasszikus határeset. -Wigner reprezentációban vizsgáltuk kölcsönható részecskék dinamikai viselkedését. -Nem-egyensúlyi betöltési szám eloszlás relaxációjának karakterisztikus idejét vizsgáltuk rendezetlen, kölcsönható, egydimenziós rendszerben. -Elektron-lyuk rendszer rekombinációs dinamikáját vizsgáltuk egydimenziós, rendezetlen félvezető esetén. -Megállapítottuk, hogy a rendezetlenségről indirekt információt kapunk elektron-lyuk párok radiatív rekombinációjából. -Anderson-féle fém-szigetelő kritikus pontjában levő rendszer szórási tulajdonságait vizsgáltuk. | -Wavelet analysis has been applied for the investigation of properties of density operators. -Inverse magnetic domains and hybrid billiards were investigated using semiclassical analysis. -We have investigated the properties of systems in Husimi representation where the classical limit may not exist. -The dynamical aspects of interacting particles have been studied using the Wigner representation. -The characteristic time scale of the relaxation of non-equilibrium occupation number distribution has been computed numerically for disordered, interacting, one-dimensional systems. -The radiative recombination dynamics of electron-hole plasmas has been investigated numerically in one-dimensional, disordered semiconductors. -It is found that the autocorrelation of the light emitted from an electron-hole recombination gives indirect information about the disorder present in semiconductors. -The scattering properties of a system which is at the metal-insulator transition of the Anderson-type has been calculated numerically

Growing Networks – Modelling the Growth of Word Association Networks for Hungarian and English

In the new era of information and communication technology, the representation of information is of increasing importance. Knowing how words are connected to each other in the mind and what processes facilitate the creation of connections could result in better optimized applications, e.g. in computer aided education or in search engines. This paper models the growth process of a word association database with an algorithm. We present the network structure of word associations for an agglutinative language and compare it with the network of English word associations. Using the real-world data so obtained, we create a model that reproduces the main features of the observed growth process and show the evolution of the network. The model describes the growth of the word association data as a mixture of a topic based process and a random process. The model makes it possible to gain insight into the overall processes which are responsible for creating an interconnected mental lexicon

Generalised thresholding of hidden variable network models with scale-free property

The hidden variable formalism (based on the assumption of some intrinsic node parameters) turned out to be a remarkably efficient and powerful approach in describing and analyzing the topology of complex networks. Owing to one of its most advantageous property - namely proven to be able to reproduce a wide range of different degree distribution forms - it has become a standard tool for generating networks having the scale-free property. One of the most intensively studied version of this model is based on a thresholding mechanism of the exponentially distributed hidden variables associated to the nodes (intrinsic vertex weights), which give rise to the emergence of a scale-free network where the degree distribution p(k) similar to k(-gamma) is decaying with an exponent of gamma = 2. Here we propose a generalization and modification of this model by extending the set of connection probabilities and hidden variable distributions that lead to the aforementioned degree distribution, and analyze the conditions leading to the above behavior analytically. In addition, we propose a relaxation of the hard threshold in the connection probabilities, which opens up the possibility for obtaining sparse scale free networks with arbitrary scaling exponent

A multimodal deep learning architecture for smoking detection with a small data approach

Introduction: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. Methods: We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Results: Our model can achieve 74\% accuracy for images and 98\% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Conclusions: Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data

Fundamental statistical features and self-similar properties of tagged networks

We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterise the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g., the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterised by the tag-assortativity exponent, which is specific to each system

Comparing the hierarchy of keywords in on-line news portals

The tagging of on-line content with informative keywords is a widespread phenomenon from scientific article repositories through blogs to on-line news portals. In most of the cases, the tags on a given item are free words chosen by the authors independently. Therefore, relations among keywords in a collection of news items is unknown. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialised ones at the bottom. Here we apply a recent, cooccurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorised low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals