TASK DEFINITION: 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.
PROBLEM: This paper describes the architecture and implementation of a rule-based grapheme to phoneme converter for Turkish. The system accepts surface form as input, outputs SAMPA mapping of the all parallel pronounciations according to the morphological analysis together with stress positions. The system has been implemented in Python.

SOLUTION: Towards Turkish ASR: Anatomy of a rule-based Turkish g2p

PROBLEM: We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multiagent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.

SOLUTION: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

PROBLEM: Parkinson’s disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson’s disease in the United States whereas the number of people suffering from Parkinson’s disease worldwide is around 5 millions. Thus, it is important to predict Parkinson’s disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson’s disease, however an increasing amount of research is being done to predict the Parkinson’s disease from non-motor symptoms that precede the motor ones. If early and reliable prediction is possible then a patient can get a proper treatment at the right time. Nonmotor symptoms considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. Developing machine learning models that can help us in predicting the disease can play a vital role in early prediction. In this paper we extend a work which used the non-motor features such as RBD and olfactory loss. Along with this the extended work also uses important biomarkers. In this paper we try to model this classifier using different machine learning models that have not been used before. We developed automated diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and Boosted Logistic Regression. It has been observed that Boosted Logistic Regression provides the best performance with an impressive accuracy of 97.159 % and the area under the ROC curve was 98.9%. Thus, it is concluded that this models can be used for early prediction of Parkinson’s disease. Keywords—Improved Accuracy, Prediction of Parkinson’s Disease, Non Motor Features, Biomarkers, Machine Learning Techniques, Boosted Logistic Regression, BayesNet, Multilayer Perceptron,

SOLUTION:
An Improved Approach for Prediction of Parkinson’s Disease using Machine Learning Techniques