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Abstract Findings from two studies examining the parent and child outcomes associated with different ways of conceptualizing natural learning environment early intervention practices are presented. One sample in each study was asked to indicate the extent to which early intervention practitioners implemented their interventions in everyday family or community activities, and one sample in each study was asked to indicate the extent to which everyday family or community activities were used as sources of child learning opportunities. Results from both studies showed that using everyday activities as sources of children's learning opportunities were associated with positive benefits, whereas practitioners' implementing their interventions in everyday activities showed little or no positive benefits, and in several cases, had negative consequences. Results are discussed in terms of the need to carefully consider how and in what manner natural learning environment practices are operationalized by early intervention practitioners.

Background  In recent years, community based therapy service providers have explored different service delivery models to optimize child and family outcomes. This qualitative study aimed to explore parents' experiences of one particular service team that adopted a strengths approach, utilizing natural learning environments.Materials and methods  Nine parents undertook in‐depth, semi‐structured interviews that were taped, transcribed, and examined using thematic content analysis. Rigour was ensured through peer and member checking, field journals and an audit trail.Results  Several key themes emerged including; families' initial experiences of the service, their views of their child with a disability, their hopes for the future, and their experiences of receiving intervention from the team.Conclusions  Parents were generally positive about the adoption of a strengths approach. 'Working together', 'being positive' and 'information exchange' were the main themes that parents' attributed to their positive experiences with the service.

In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights. This problem relates, for example, to neuronal morphology: synapses which are functionally equivalent in terms of their impact on somatic firing can differ substantially in spine size due to their different positions along the dendritic tree. Classical theories based on Euclidean-gradient descent can easily lead to inconsistencies due to such parametrization dependence. The issues are solved in the framework of Riemannian geometry, in which we propose that plasticity instead follows natural-gradient descent. Under this hypothesis, we derive a synaptic learning rule for spiking neurons that couples functional efficiency with the explanation of several well-documented biological phenomena such as dendritic democracy, multiplicative scaling, and heterosynaptic plasticity. We therefore suggest that in its search for functional synaptic plasticity, evolution might have come up with its own version of natural-gradient descent.

Abstract. Modern natural hazards research requires dealing with several uncertainties that arise from limited process knowledge, measurement errors, censored and incomplete observations, and the intrinsic randomness of the governing processes. Nevertheless, deterministic analyses are still widely used in quantitative hazard assessments despite the pitfall of misestimating the hazard and any ensuing risks. In this paper we show that Bayesian networks offer a flexible framework for capturing and expressing a broad range of uncertainties encountered in natural hazard assessments. Although Bayesian networks are well studied in theory, their application to real-world data is far from straightforward, and requires specific tailoring and adaptation of existing algorithms. We offer suggestions as how to tackle frequently arising problems in this context and mainly concentrate on the handling of continuous variables, incomplete data sets, and the interaction of both. By way of three case studies from earthquake, flood, and landslide research, we demonstrate the method of data-driven Bayesian network learning, and showcase the flexibility, applicability, and benefits of this approach. Our results offer fresh and partly counterintuitive insights into well-studied multivariate problems of earthquake-induced ground motion prediction, accurate flood damage quantification, and spatially explicit landslide prediction at the regional scale. In particular, we highlight how Bayesian networks help to express information flow and independence assumptions between candidate predictors. Such knowledge is pivotal in providing scientists and decision makers with well-informed strategies for selecting adequate predictor variables for quantitative natural hazard assessments.

Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions. ; This work is supported in part by the Catalan Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR) through the FI PhD grant; the Spanish Ministerio de Ciencia e Innovación and by the Agencia Estatal de Investigación through the Ramón y Cajal grant and the project PCIN-2017-079; and by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 947657). ; Peer Reviewed ; Postprint (published version)

Addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence and machine learning. Recognizing the necessity for a multifaceted approach, the book advocates the four R s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of disaster management

"In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML).This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four 'R's - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management.This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations."--

The relationship between natural capital and economic growth is an open debate in the field of economic development. Is an abundance of natural resources a blessing or a curse for economic performanceThe field of Economic History offers an excellent vantage to explore the relevance of institutions, technical progress and supply-demand drivers. Natural Resources and Economic Growth contains theoretical and empirical articles by leading scholars who have studied this subject in different historical periods from the 19th century to the present day and in different parts of the world. Part I presents the theoretical issues and discusses the meaning of the "curse" and the relevance of the historical perspective. Part II captures the diversity of experiences, presenting thirteen independent case studies based on historical results from North and South America, Africa, Asia, Oceania and Europe. This book emphasizes that an abundance of natural resources is not a fixed situation. It is a process that reacts to changes in the structure of commodity prices and factor endowments, and progress requires capital, labour, technical change and appropriate institutional arrangements. This abundance is not a given, but is part of the evolution of the economic system. History shows that institutional quality is the key factor to deal with abundant natural resources and, especially, with the rents derived from their use and exploitation. This wide ranging volume will be of great relevance to all those with an interest in economic history, development, economic growth, natural resources, world history and institutional economics