The Theory Of Linguistic Channels In Alphabetical Texts

The Theory of Linguistic Channels in Alphabetical Texts

This book is a collection of papers on a mathematical/statistical theory concerning the deep-language structure of alphabetical texts. The theory does not follow the actual paradigm of linguistic studies, which consider neither Shannon’s communication theory nor the fundamental connection that some linguistic parameters have with the reading skill and short-term memory capacity of readers. The book proposes to young researchers and students – in the fields of cognitive psychology, theory of communication, information theory, phonics and linguistics, history of modern and ancient literatures, and stylometry – a possible theoretical framework which could allow one to further research the fundamental mathematical structure of human language. This research might enable academics to devise a mathematical theory that includes meaning, the great absent element – since Shannon’s times – in our mathematical theories of human communication.

THEORY OF LINGUISTIC CHANNELS IN ALPHABETICAL TEXTS.

The Beauty of Mathematics in Computer Science

The Beauty of Mathematics in Computer Science explains the mathematical fundamentals of information technology products and services we use every day, from Google Web Search to GPS Navigation, and from speech recognition to CDMA mobile services. The book was published in Chinese in 2011 and has sold more than 600,000 copies. Readers were surprised to find that many daily-used IT technologies were so tightly tied to mathematical principles. For example, the automatic classification of news articles uses the cosine law taught in high school. The book covers many topics related to computer applications and applied mathematics including: Natural language processing Speech recognition and machine translation Statistical language modeling Quantitive measurement of information Graph theory and web crawler Pagerank for web search Matrix operation and document classification Mathematical background of big data Neural networks and Google’s deep learning Jun Wu was a staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web Search Algorithms and was responsible for many Google machine learning projects. He wrote official blogs introducing Google technologies behind its products in very simple languages for Chinese Internet users from 2006-2010. The blogs had more than 2 million followers. Wu received PhD in computer science from Johns Hopkins University and has been working on speech recognition and natural language processing for more than 20 years. He was one of the earliest engineers of Google, managed many products of the company, and was awarded 19 US patents during his 10-year tenure there. Wu became a full-time VC investor and co-founded Amino Capital in Palo Alto in 2014 and is the author of eight books.