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This book is a comprehensive study on the phonetic characteristics of citation tones in Chaoshan Chinese. It presents the tonal patterns of 65 localities in the Chaoshan area under the “multiple-register and four-level” tonal model. Three case studies are conducted to delve into the evolutionary paths of Chaoshan tones. This book not only provides a large-scale typological study on Chaoshan Chinese, but also offers a good example of how to figure out the evolutionary paths of tones from the perspective of variation. The natural alliance of phonetics, historical linguistics, sociolinguistics, and dialect geography is reinforced. It is also suggested in this book that the joint use of these four disciplines is very promising for the study of Chinese.
This book has its unique value as the pioneer study on the regional variants of Mandarin by experimental phonetics methods, sociophonetic methods, and AI assisted big data methods. Dialectal Mandarins are commonly well-recognized variations for standards Mandarin that have regional characteristics. This book is divided into four parts. The first part introduces Chinese, Chinese dialects, and Mandarin tones. In part two, the book makes a comprehensive and systematic comparison among Beijing Mandarin, Shanghai Mandarin, and Guangzhou Mandarin as the three major Mandarin variants by both the experimental phonetic methods and by using deep learning method. In the deep learning chapter, this book explores whether deep learning can recognize regional dialects patterns from the large amounts of data, and whether it can successfully identify the tonal system of each region’s dialects. The experimental results show that deep learning performs perfectly well in regional dialect recognition and tonal system learning. Liu Yuxuan, a student from USST, provided important technical support for the deep learning experiment. The third part further deepens the research perspective, and studies the geographically close subregional Mandarin. This part involves both acoustic research and perceptual research on three geographically close subregional dialects of Mandarin. The last part is a report on two experiments investigating dialectal experience’s role in predicting listeners’ subjective depiction on both non-categorical and categorical tonal variants of the official language, Putonghua. The dialectal resources recorded in this book are also valuable. The book is supposed to contribute to new progress in academic study on language variation, language evolution, experimental phonetics methods, as well as AI programming.
This two-part volume provides a collection of 27 linguistic studies and contributions that shed light on the evolution of different Englishes world-wide (varieties, learner Englishes, dialects, creoles) from a broad spectrum of different perspectives, including both synchronic and diachronic approaches. What makes the volume unique is that it is the first-ever contribution to the field which includes a section exclusively commited towards testing, discussing and refining Schneider’s (2007) Dynamic Model against recent realities of English world-wide (Part 1). These realities include a wide variety of case studies ranging from regions (socio)linguistically as diverse as South Africa, the Phillipines, Cyprus or Germany. Part 2 goes beyond the Dynamic Model and offers both empirical and theoretical perspectives on the evolution of World Englishes. In doing so, it provides contributions with a theoretical focus on the topic as well as cross-varietal accounts; it sheds light on individual Englishes from different geographical regions and offers new perspectives on “old” varieties.
In this book, Ljiljana Progovac proposes a gradualist, adaptationist approach to the evolution of syntax, subject to natural selection. She provides a specific framework for its study, combining the fields of evolutionary biology, theoretical syntax, typology, neuroscience, and genetics. The author pursues an internal reconstruction of the stages of grammar based on the syntactic theory associated with Chomskyan Minimalism and arrives at specific, testable hypotheses, which are then corroborated by an abundance of theoretically analysed 'living fossils' drawn from a variety of languages. Her approach demonstrates that these fossil structures do not just coexist alongside more modern structures, but are in fact built into the very foundation of more complex structures, leading to quirks and complexities that are suggestive of a gradualist evolutionary scenario. By reconstructing a particular path along which syntax evolved, Evolutionary Syntax sheds light on the crucial properties of language design itself, as well as on the major parameters of crosslinguistic variation. As a result, this reconstruction can be meaningfully correlated with both the hominin timeline and the ever-growing body of genetic evidence that is available.
This book constitutes the refereed joint proceedings of six workshops on evolutionary computing, EvoWorkshops 2005, held in Lausanne, Switzerland in March/April 2005. The 56 revised full papers presented were carefully reviewed and selected from a total of 143 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computation in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.
Why information is the unifying principle that allows us to understand the evolution of complexity in nature More than 150 years after Darwin’s revolutionary On the Origin of Species, we are still attempting to understand and explain the amazing complexity of life. Although we now know how evolution proceeds to build complexity from simple ingredients, quantifying this complexity is still a difficult undertaking. In this book, Christoph Adami offers a new perspective on Darwinian evolution by viewing it through the lens of information theory. This novel theoretical stance sheds light on such matters as how viruses evolve drug resistance, how cells evolve to communicate, and how intelligence evolves. By this account, information emerges as the central unifying principle behind all of biology, allowing us to think about the origin of life—on Earth and elsewhere—in a systematic manner. Adami, a leader in the field of computational biology, first provides an accessible introduction to the information theory of biomolecules and then shows how to apply these tools to measure information stored in genetic sequences and proteins. After outlining the experimental evidence of the evolution of information in both bacteria and digital organisms, he describes the evolution of robustness in viruses; the cooperation among cells, animals, and people; and the evolution of brains and intelligence. Building on extensive prior work in bacterial and digital evolution, Adami establishes that (expanding on Dobzhansky’s famous remark) nothing in biology makes sense except in the light of information. Understanding that information is the foundation of all life, he argues, allows us to see beyond the particulars of our way of life to glimpse what life might be like in other worlds.
This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Artificial Evolution, EA 2017, held in Paris, France, in October 2017. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmics and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences, ...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms...), memetic algorithms, multi-objective optimisation, constraint handling, parallel algorithms,, dynamic optimization, machine learning and hybridization with other soft computing techniques.