Language Matters: A Guide To Everyday Questions About Language

Is Ebonics really a dialect or simply bad English? Do women and men speak differently? Will computers ever really learn human language? Does offensive language harm children? These are only a few of the issues surrounding language that crop up every day. Most of us have very definite opinions on these questions one way or another. Yet as linguists Donna Jo Napoli and Vera Lee-Schoenfeld point out in this short and thoroughly readable volume, many of our most deeply held ideas about the nature of language and its role in our lives are either misconceived or influenced by myths and stereotypes Language Matters provides a highly informative tour of the world of language, examining these and other vexing and controversial language-related questions. Throughout, Napoli and Lee-Schoenfeld encourage and lead the reader to use common sense and everyday experience rather than preconceived notions or technical linguistic expertise. Both their questions and their conclusions are surprising, sometimes provocative, and always entertaining. This thoroughly revised second edition updates the book with a new coauthor and includes new chapters on language and power, language extinction, and what it is linguists actually do. Language Matters is sure to engage both general readers and students of language and linguistics at any level

Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.Comment: 4 pages, 2 figur

Heterodox production and cost theory of the business enterprise

Heterodox economists long complained about having no systematic alternative to neoclassical production and cost theory. This paper deals with this complaint. That is, it presents a theory of production and costs of the business enterprise that is a complete alternative to the neoclassical theory of production and costs of the firm.heterodox theory, accounting rules, structure of production and costs, segmented plants, direct costs, shop expenses, enterprise expenses

Social surplus approach and heterodox economics

Given the emphasis on social provisioning in heterodox economics, two of its central theoretical organizing principles are the concepts of the total social product and the social surplus. This appears to link heterodox economics to the social surplus approach associated with the classical economists and currently with Sraffian economists. However, heterodox economics connects agency with the social surplus and the social product, which the Sraffians reject as they take the level and composition of the social product as given. Therefore the different theoretical approach regarding the social surplus taken in heterodox economics may generate a different but similar way of theorizing about a capitalist economy. To explore this difference is the aim of the paper. Thus the paper is divided into four parts and a conclusion. In the first section social provisioning and the social surplus is introduced. In the second section, the Sraffian social surplus approach is delineated while in the third section the heterodox social surplus approach is delineated. In the fourth section of the paper, some of the implications emerging from the differences between the two approaches are discussed. The paper is concluded in the final section.social surplus, social product, social provisioning, agency, Sraffian economics, heterodox economics

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS