Identifying and Mapping the Global Research Output on Coronavirus Disease: A Scientometric Study

The paper explores and analyses the trend of world literature on "Coronavirus Disease" in terms of the output of research publications as indexed in the Science Citation Index Expanded (SCI-E) of Web of Science during the period from 2011 to 2020. The study found that 6071 research records have been published on Coronavirus Disease till March 20, 2020. The various scientometric components of the research records published in the study period were studied. The study reveals the various aspects of Coronavirus Disease literature such as year wise distribution, relative growth rate, doubling time of literature, geographical wise, organization wise, language wise, form wise , most prolific authors, and source wise. The highest number of articles was published in the year 2019, while lowest numbers of research article were reported in the year 2020. Further, the relative growth rate is gradually increases and on the other hand doubling time decreases. Most of the research publications are published in English language and most of the publications published in the form of research articles. USA is the highest contributor to the field of Coronavirus Disease literature.

Testing Lotka's Law and Pattern of Author Productivity in the Scholarly Publications of Artificial Intelligence

Artificial intelligence has changed our day to day life in multitude ways. AI technology is rearing itself as a driving force to be reckoned with in the largest industries in the world. AI has already engulfed our educational system, our businesses and our financial establishments. The future is definite that machines with artificial intelligence will soon be captivating over trained manual work that now is mostly cared by humans. Machines can carry out human-like tasks by new inputs as artificial intelligence makes it possible for machines to learn from experience. AI data from web of science database from 2008 to 2017 have been mapped to depict the average growth rate, relative growth rate, contribution made by authors in the view of research productivity, authorship pattern and collaboration of AI literature. The Lotka's law on authorship productivity of AI literature has been tested to confirm the applicability of the law to the present data set. A K-S test was applied to measure the degree of agreement between the distribution of the observed set of data against the inverse general power relationship and the theoretical value of {\alpha} =2. It is found that the inverse square law of Lotka follow as such.

Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks

Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation. In this paper, we propose Competitive Dense Fully Convolutional Networks (CDFNet) by introducing competitive maxout activations in place of naive feature concatenation for inducing competition amongst layers. Within CDFNet, we propose two architectural contributions, namely competitive dense block (CDB) and competitive unpooling block (CUB) to induce competition at local and global scales for short and long-range skip connections respectively. This extension is demonstrated to boost learning of specialized sub-networks targeted at segmenting specific anatomies, which in turn eases the training of complex tasks. We present the proof-of-concept on the challenging task of whole body segmentation in the publicly available VISCERAL benchmark and demonstrate improved performance over multiple learning and registration based state-of-the-art methods.