The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.
A new approach to network decomposition problems (and, hence, to classification problems, presented in network form) is suggested. Opposite to the conventional approach, consisting in construction of one, “the most correct” decomposition (classification), the suggested approach is focused on construction of a family of classifications. Basing on this family, two numerical indices are introduced and calculated. The suggested indices describe the complexity of the initial classification problem as whole. The expedience and applicability of the elaborated approach are illustrated by two well-known and important cases: political voting body and stock market. In both cases the presented results cannot be obtained by other known methods. It confirms the perspectives of the suggested approach.
Речь идет о применении новых вероятностно-статистических моделей к сравнительному анализу разноязычных метрически организованных (стихотворных) текстов.
We have observed unusual plasma formations (UPFs) in artificial clouds of charged water droplets using a high-speed infrared camera operating in conjunction with a high-speed visible-range camera. Inferred plasma parameters were close to those of long-spark leaders observed in the same experiments, while the channel morphology was distinctly different from that of leaders, so that UPFs can be viewed as a new type of in-cloud discharge. These formations can occur in the absence of spark leaders and appear to be manifestations of collective processes building, essentially from scratch, a complex hierarchical network of interacting channels at different stages of development (some of which are hot and live for milliseconds). We believe that the phenomenon should commonly occur in thunderclouds and might give insights on the missing link in the still poorly understood lightning initiation process.
The topic of recommender systems is rapidly gaining interest in the user-behaviour modeling research domain. Over the years, various recommender algorithms based on different mathematical models have been introduced in the literature. Researchers interested in proposing a new recommender model or modifying an existing algorithm should take into account a variety of key performance indicators, such as execution time, recall and precision. Till date and to the best of our knowledge, no general cross-validation scheme to evaluate the performance of recommender algorithms has been developed. To fill this gap we propose an extension of conventional cross-validation. Besides splitting the initial data into training and test subsets, we also split the attribute description of the dataset into a hidden and visible part. We then discuss how such a splitting scheme can be applied in practice. Empirical validation is performed on traditional user-based and item-based recommender algorithms which were applied to the MovieLens dataset.
Излагается оригинальный метод разработки алгоритмов семантико-синтаксического анализа текстов на естественном языке (ЕЯ). Этот метод является расширением метода, предложенного В.А. Фомичевым в монографии, опубликованной издательством Шпрингер в 2010 году. Для построения семантических представлений текстов используется класс СК-языков. Входные тексты могут принадлежать по крайней мере широким и практически интересным подъязыкам русского, английского, французского и немецкого языков. Заключительная часть статьи описывает применение разработанного метода к проектированию ЕЯ-интерфейса прикладной программной системы, выполняющей различные действия. Разработанный ЕЯ-интерфейс NLC-1 (Естественно-Языковый Коммандер – Версия 1) реализован с помощью языка функционального программирования Хаскел (Haskell).
We describe a new recommender system for the Russian interactive radio network FMhost. The new recommender model combines collaborative and user-based approaches. The system extracts information from tags of listened tracks for matching user and radio station profiles and follows an adaptive online learning strategy based on user history. We also provide some basic examples and describe the quality of service evaluation methodology.
The authors propose a new statistical unconstraining method which is based on the construction of the distribution function for the censored demand and application of the maximum likelihood approach to estimate distribution parameters. Numerical results are presented of comparative analysis of existing unconstraining methods and the method advocated in the paper. It is demonstrated that the new method has proven to be more efficient in the case of a high percentage of observed censored elements of sample data. Yet another important advantage of the method connected to the fact that it enables one to process the situation of censoring information incompleteness when some elements of the observed sample data are known to be censored or not and for the others this information is not available. Mathematical computer environment Wolfram Mathematica has been used for obtaining all the results presented in the paper.