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.

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 Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property

This paper presents Centre for Development of Advanced Computing Mumbai's (CDACM) submission to NLP Tools Contest on Statistical Machine Translation in Indian Languages (ILSMT) 2015 (collocated with ICON 2015). The aim of the contest was to collectively explore the effectiveness of Statistical Machine Translation (SMT) while translating within Indian languages and between English and Indian languages. In this paper, we report our work on all five language pairs, namely Bengali-Hindi (bn-hi), Marathi-Hindi (mrhi), Tamil-Hindi (ta-hi), Telugu-Hindi (tehi), and English-Hindi (en-hi) for Health, Tourism and General domains. We have used suffix separation, compound splitting and preordering prior to SMT training and testing.
Statistical Machine Translation for Indian Languages: Mission Hindi 2

A Semantic Compositional Network (SCN) is developed<lb>for image captioning, in which semantic concepts (i.e., tags)<lb>are detected from the image, and the probability of each tag<lb>is used to compose the parameters in a long short-term mem-<lb>ory (LSTM) network. The SCN extends each weight matrix of<lb>the LSTM to an ensemble of tag-dependent weight matrices.<lb>The degree to which each member of the ensemble is used<lb>to generate an image caption is tied to the image-dependent<lb>probability of the corresponding tag. In addition to caption-<lb>ing images, we also extend the SCN to generate captions for<lb>video clips. We qualitatively analyze semantic composition<lb>in SCNs, and quantitatively evaluate the algorithm on three<lb>benchmark datasets: COCO, Flickr30k, and Youtube2Text.<lb>Experimental results show that the proposed method signifi-<lb>cantly outperforms prior state-of-the-art approaches, across<lb>multiple evaluation metrics.
Semantic Compositional Networks for Visual Captioning