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The advent of behavior-independent measures of cognition and major progress in experimental designs have led to substantial advances in the investigation of infant language learning mechanisms. Research in the last two decades has shown that infants are very efficient users of perceptual and statistical cues in order to extract linguistic units and regular patterns from the speech input. This has lent support for learning-based accounts of language acquisition that challenge traditional nativist views. Still, there are many open questions with respect to when and how specific patterns can be learned and the relevance of different types of input cues. For example, first steps have been made to identify the neural mechanisms supporting on-line extraction of words and statistical regularities from speech. Here, the temporal cortex seems to be a major player. How this region works in concert with other brain areas in order to detect and store new linguistic units is a question of broad interest. In this Research Topic of Frontiers in Language Sciences, we bring together experimental and review papers across linguistic domains, ranging from phonology to syntax that address on-line language learning in infancy. Specifically, we focused on papers that explore one of the following or related questions: How and when do infants start to segment linguistic units from the speech input and discover the regularities according to which they are related to each other? What is the role of different linguistic cues during these acquisition stages and how do different kinds of information interact? How are these processes reflected in children’s behavior, how are they represented in the brain and how do they unfold in time? What are the characteristics of the acquired representations as they are established, consolidated and stored in long-term memory? By bringing together behavioral and neurophysiological evidence on language learning mechanisms, we aim to contribute to a more complete picture of the expeditious and highly efficient early stages of language acquisition and their neural implementation.

Language acquisition. --- Language and languages --- language learning --- statistical learning --- Prosody --- language development --- phonotactics --- artificial grammar --- word learning --- speech segmentation --- phonology --- Physiological aspects. --- language learning --- statistical learning --- Prosody --- language development --- phonotactics --- artificial grammar --- word learning --- speech segmentation --- phonology

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This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.

Statistics. --- Machine learning. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical Learning. --- Machine Learning.

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The advent of behavior-independent measures of cognition and major progress in experimental designs have led to substantial advances in the investigation of infant language learning mechanisms. Research in the last two decades has shown that infants are very efficient users of perceptual and statistical cues in order to extract linguistic units and regular patterns from the speech input. This has lent support for learning-based accounts of language acquisition that challenge traditional nativist views. Still, there are many open questions with respect to when and how specific patterns can be learned and the relevance of different types of input cues. For example, first steps have been made to identify the neural mechanisms supporting on-line extraction of words and statistical regularities from speech. Here, the temporal cortex seems to be a major player. How this region works in concert with other brain areas in order to detect and store new linguistic units is a question of broad interest. In this Research Topic of Frontiers in Language Sciences, we bring together experimental and review papers across linguistic domains, ranging from phonology to syntax that address on-line language learning in infancy. Specifically, we focused on papers that explore one of the following or related questions: How and when do infants start to segment linguistic units from the speech input and discover the regularities according to which they are related to each other? What is the role of different linguistic cues during these acquisition stages and how do different kinds of information interact? How are these processes reflected in children’s behavior, how are they represented in the brain and how do they unfold in time? What are the characteristics of the acquired representations as they are established, consolidated and stored in long-term memory? By bringing together behavioral and neurophysiological evidence on language learning mechanisms, we aim to contribute to a more complete picture of the expeditious and highly efficient early stages of language acquisition and their neural implementation.

Language acquisition. --- Language and languages --- Physiological aspects. --- language learning --- statistical learning --- Prosody --- language development --- phonotactics --- artificial grammar --- word learning --- speech segmentation --- phonology

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This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.

Artificial intelligence --- Data mining. --- Machine learning. --- Data Science. --- Data Mining and Knowledge Discovery. --- Statistical Learning. --- Data processing. --- Aprenentatge automàtic --- Estadística --- Desenvolupament sostenible

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This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Agricultural science --- Life sciences: general issues --- Botany & plant sciences --- Animal reproduction --- Probability & statistics --- open access --- Statistical learning --- Bayesian regression --- Deep learning --- Non linear regression --- Plant breeding --- Crop management --- multi-trait multi-environments models