In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: This paper describes the realization of the Ontology Web Search Engine. The Ontology Web Search Engine is realizable as independent project and as a part of other projects. The main purpose of this paper is to present the Ontology Web Search Engine realization details as the part of the Semantic Web Expert System and to present the results of the Ontology Web Search Engine functioning. It is expected that the Semantic Web Expert System will be able to process ontologies from the Web, generate rules from these ontologies and develop its knowledge base.

A: REALIZATION OF ONTOLOGY WEB SEARCH ENGINE
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Q: Here, we propose a brain-inspired winner-take-all emotional neural network (WTAENN) and prove the universal approximation property for the novel architecture. WTAENN is a single layered feedforward neural network that benefits from the excitatory, inhibitory, and expandatory neural connections as well as the winner-take-all (WTA) competitions in the human brain’s nervous system. The WTA competition increases the information capacity of the model without adding hidden neurons. The universal approximation capability of the proposed architecture is illustrated on two example functions, trained by a genetic algorithm, and then applied to several competing recent and benchmark problems such as in curve fitting, pattern recognition, classification and prediction. In particular, it is tested on twelve UCI classification datasets, a facial recognition problem, three real world prediction problems (2 chaotic time series of geomagnetic activity indices and wind farm power generation data), two synthetic case studies with constant and nonconstant noise variance as well as k-selector and linear programming problems. Results indicate the general applicability and often superiority of the approach in terms of higher accuracy and lower model complexity, especially where low computational complexity is

A: A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property
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Q: Deep Neural Network (DNN) are currently of great interest in research and application. The training of these networks is a compute intensive and time consuming task. To reduce training times to a bearable amount at reasonable cost we extend the popular Caffe toolbox for DNN with an efficient distributed memory communication pattern. To achieve good scalability we emphasize the overlap of computation and communication and prefer fine granular synchronization patterns over global barriers. To implement these communication patterns we rely on the the ”Global address space Programming Interface” version 2 (GPI-2) communication library. This interface provides a light-weight set of asynchronous one-sided communication primitives supplemented by non-blocking fine granular data synchronization mechanisms. Therefore, CaffeGPI is the name of our parallel version of Caffe. First benchmarks demonstrate better scaling behavior compared with other extensions, e.g., the IntelTMCaffe. Even within a single symmetric multiprocessing machine with four graphics processing units, the CaffeGPI scales better than the standard Caffe toolbox. These first results demonstrate that the use of standard High Performance Computing (HPC) hardware is a valid cost saving approach to train large DDNs. I/O is an other bottleneck to work with DDNs in a standard parallel HPC setting, which we will consider in more detail in a forthcoming paper.

A:
Using GPI-2 for Distributed Memory Paralleliziation of the Caffe Toolbox to Speed up Deep Neural Network Training
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