Simulations between Network Topologies in Networks of Evolutionary Processors

In this paper, we propose direct simulations between a given network of evolutionary processors with an arbitrary topology of the underlying graph and a network of evolutionary processors with underlying graphs—that is, a complete graph, a star graph and a grid graph, respectively. All of these simulations are time complexity preserving—namely, each computational step in the given network is simulated by a constant number of computational steps in the constructed network. These results might be used to efficiently convert a solution of a problem based on networks of evolutionary processors provided that the underlying graph of the solution is not desired.

Dimensional reduction of higher-point conformal blocks

Recently, with the help of Parisi-Sourlas supersymmetry an intriguing relation was found expressing the four-point scalar conformal block of a (d − 2)-dimensional CFT in terms of a five-term linear combination of blocks of a d-dimensional CFT, with constant coefficients. We extend this dimensional reduction relation to all higher-point scalar conformal blocks of arbitrary topology restricted to scalar exchanges. We show that the constant coefficients appearing in the finite term higher-point dimensional reduction obey an interesting factorization property allowing them to be determined in terms of certain graphical Feynman-like rules and the associated finite set of vertex and edge factors. Notably, these rules can be fully determined by considering the explicit power-series representation of just three particular conformal blocks: the four-point block, the five-point block and the six-point block of the so-called OPE/snowflake topology. In principle, this method can be applied to obtain the arbitrary-point dimensional reduction of conformal blocks with spinning exchanges as well. We also show how to systematically extend the dimensional reduction relation of conformal partial waves to higher-points.

Frequency scheduling algorithm with the allocation of the main and additional frequency bands.

The subject of this research is the frequency planning algorithm for networks with an arbitrary topology of links over radio channels. The algorithm determines the total number of non-overlapping frequency ranges for the entire network and provides the distribution of each frequency range between communication nodes. The algorithm consists of two stages: at the first stage, there is a search and simultaneous distribution of frequency channels, the so-called main frequency range, as a result, only one frequency range is allocated to each node; at the second stage, additional frequency channels are searched for, which can be used by a separate subset of nodes, thus , some nodes can use more than one frequency range, but several at once. The novelty of this research lies in the developed frequency planning algorithm for wireless communication systems with an arbitrary topology of communications over radio channels. The result of the operation of the algorithm for a wireless communication system is the allocation of radio frequencies for communication nodes from the common frequency band allocated for the wireless communication system, in terms of reuse, eliminating the effect of interference. The result for communication nodes is the allocation of a baseband and an additional frequency band, taking into account the topology of the radio network, which can be used by a separate subset that makes wireless communication systems resistant to narrowband random interference.

Finding a Universal Execution Strategy for Model Transformation Networks

When using multiple models to describe a (software) system, one can use a network of model transformations to keep the models consistent after changes. No strategy exists, however, to orchestrate the execution of transformations if the network has an arbitrary topology. In this paper, we analyse how often and in which order transformations need to be executed. We argue why linear execution bounds are too restrictive to be useful in practice and prove that there is no upper bound for the number of necessary executions. To avoid non-termination, we propose a conservative strategy that makes execution failures easier to understand. These insights help developers and users of transformation networks to understand under which circumstances their networks can terminate. Additionally, the proposed strategy helps them to find the cause when a network cannot restore consistency.

РЕАЛИЗАЦИЯ ГЛУБОКИХ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ НА СБИС 1879ВМ8Я

В статье рассматриваются вопросы реализации глубоких сверточных нейронных сетей на отечественной СБИС 1879ВМ8Я. Приводятся принципы программирования многопроцессорной гетерогенной системы, принципы оптимизации вычислений с учетом архитектурных особенностей СБИС. Описывается специализированный планировщик для портирования пользовательских сверточных нейронных сетей с произвольной топологией на архитектуру 1879ВМ8Я. This article is devoted to implementation of deep convolutional neural networks on the NM6408 ASIC. The principles of programming the multiprocessor heterogeneous system and optimizing the calculations concerning the architecture of ASIC have been provided. A specialized scheduler for porting custom convolutional neural networks with an arbitrary topology to the NM6408 architecture has been proposed.

An Eventually Perfect Failure Detector for Networks of Arbitrary Topology Connected with ADD Channels Using Time-To-Live Values

We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants [Formula: see text], [Formula: see text], not known to the processes, such that for every [Formula: see text] consecutive messages sent in one direction, at least one is delivered within time [Formula: see text]. The best previous implementation used messages of bounded size, but exponential in [Formula: see text], the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size [Formula: see text].