Due to constant health awareness and readily available information on usefulness of different diet and their direct link with health,

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Community Question Answering (cQA) services like Yahoo! Answers 1 , Baidu Zhidao 2 , Quora 3 , StackOverflow 4 etc. provide a platform for interaction with experts and help users to obtain precise and accurate answers to their questions. The time lag between the user posting a question and receiving its answer could be reduced by retrieving similar historic questions from the cQA archives. The main challenge in this task is the "lexicosyntactic" gap between the current and the previous questions. In this paper, we propose a novel approach called "Siamese Convolutional Neural Network for cQA (SCQA)" to find the semantic similarity between the current and the archived questions. SCQA consist of twin convolutional neural networks with shared parameters and a contrastive loss function joining them. SCQA learns the similarity metric for question-question pairs by leveraging the question-answer pairs available in cQA forum archives. The model projects semantically similar question pairs nearer to each other and dissimilar question pairs farther away from each other in the semantic space. Experiments on large scale reallife "Yahoo! Answers" dataset reveals that SCQA outperforms current state-of-theart approaches based on translation models, topic models and deep neural network https://answers.yahoo.com/

Advanced and sustainable energy storage technologies with tailorable electrochemically active materials platform are the present research dominancy toward an urgent global need for electrical vehicles and portable electronics. Moreover, intensive efforts are given to screen the widely available low-cost materials with a focus to achieve superior electrochemical performance for the fabrication of energy storage devices. Transition metal-based sulfides have prodigious technological credibility due to their compositional-and morphological-based tunable electrochemical properties. Here the significant advances and present state-ofthe-art of such assured materials in different energy storage devices are discussed. Assessment of the intensive work invested in the progress of transition metals such as V, Mn, Fe, Co, Ni, Cu, Zn Mo, and W based sulfides along with their structural/compositional engineering and addressable aspects for electrochemical performance enhancement are highlighted. Additionally, discussions on critical strategies for decisive mechanistic and kinetic views for charge storage phenomena with key challenges, such as volume expansions, low stability, and sluggish kinetics, are discussed. Finally, the challenges and future prospects demands for strategic approaches of such materials with prominence in futuristic directions are concluded.