Books on the topic 'Uncertain structural processes'

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.

The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

The Oxford Handbook of Energy and Society offers a timely and much-needed synthesis of recent developments in sociological analysis of energy-society relations, representing a wide breadth of contributors in sociology and related disciplines from across the globe. Regional case studies of different energy resources are featured, as are the roles of politics, markets, technology, social movements, and consumers, all contributing to a complex systems perspective on the uncertain future of energy-society relations. The volume is divided into seven sections. Section One includes chapters that highlight key contemporary dynamics and theoretical contributions in this field of scholarship. Following this is a section showcasing structural perspectives on energy-society relations, including chapters describing the persistent material and geopolitical relevance of fossil fuels. Section Three highlights research on consumers and consumption processes, while Section Four draws attention to emerging research on the inequitable distribution of energy access, and energy poverty. Section Five includes chapters that focus on the influence of publics and civil society in contemporary energy-society relations. Section Six offers chapters that focus on current trends in energy politics, and finally, in the concluding section we offer a selection of chapters that highlight some emerging trends that may have potential to generate—or constrain—significant shifts in energy-society relationships. While offering a diversity of perspectives and empirical research, contributors to this volume agree on a number of key issues that offer important insights into the future of energy-society relations, including the growing instability imposed by fossil-fuel dependence, and challenges and innovations associated with a renewable energy transition.

Mapudungun, an unclassified language of southern Chile and south-central Argentina spoken by a somewhat uncertain but sizeable number of speakers, has word-formation phenomena that deserve to be called polysynthetic according to most of the (sometimes mutually exclusive) definitions of this term found in the descriptive and typological literature. Polypersonalism, productive nominal incorporation, a limited amount of lexical affixation, alongside significant grammatical affixation, and especially root-serializing/compounding processes lead to long and complex templatically structured verbal predicates that markedly contrast, not only with rather simple nouns in the same language, but also with predicates in many other languages of the region. This chapter describes the major word-formation processes of Mapudungun paying special attention to the typologies of polysynthesis that have been proposed in previous studies on the subject.

This chapter is concerned with those disorders in which the primary pathological process affects skeletal muscle, for which in everyday clinical practice the term myopathy is convenient shorthand. However, it must be stressed that diseases of the motor nerves and neuromuscular junction can produce an identical clinical picture to several of the myopathies, and this will be emphasized many times throughout the chapter when considering differential diagnosis. Indeed sometimes, despite one’s best efforts, one is left uncertain as to whether the primary disease process is in the nerves or muscles—it may be that in some conditions the disease process directly affects both nerves and muscles. The intimate relationship, both structural and functional, between nerves and the muscles they innervate means that disease of one may have a profound effect on the other—the most striking example is the change that occurs to skeletal muscle fibre-type distribution in denervation.

This chapter covers the resolution of US insured depository institutions, which is governed primarily by sections 11 and 13 of the Federal Deposit Insurance Act. It discusses certain background issues, including the chartering authorities of the institutions that are subject to resolution authority, the deposit insurance requirement, the structure of the FDIC’s resolution unit, the administrative nature of the FDIC resolution process, and the relatively high level of legal uncertainty in this area of US law. This chapter then describes the supervisory and other tools designed to prevent troubled banks and thrifts from failing, and also discusses the resolution process, the recapitalization (bail-in) within resolution strategy, and the ancillary claims process for claims left behind in the receivership.

Explaining the Future addresses the questions “will this new technology solve the problem that its inventors claim it will,” “will it succeed for any application at all,” “can we narrow down the options before we spend a lot of money on development,” and “how do we persuade colleagues, investors, clients, or readers of our technical reasoning?” Whether the person answering these questions is a researcher, a consultant, a venture capitalist, or a CTO, they will need to be able to answer them clearly and systematically. Most learn these skills only through years of experience. However, by making them explicit, this book makes the learning process more efficient and speeds its readers toward higher-level careers. First, it will provide the tools to think through matching new (and old) technologies, materials, and processes with applications: it covers the questions to ask, the resources needed to answer them, and who deserves trust. Then, it discusses analyzing the information that has been gathered in a systematic way and dealing with uncertainty. Next, there are chapters on communication, including tailoring documents to a specific audience, making a persuasive and structured technical argument, and writing an explanation that is credible and easy to follow. Finally, the book includes a case study: a real worked example that goes from an idea through the twists and turns of the research and analysis process to a final report.

No universally accepted notion of evolvability is available, focus being alternatively put onto either genetic or phenotypic change. The heuristic power of this concept is best found when considering the intricacies of the genotype→phenotype map, which is not necessarily predictable, expression of variation depending on the structure of gene networks and especially on the modularity and robustness of developmental systems. We can hardly ignore evolvability whenever studying the role of cryptic variation in evolution, the often pervious boundary between phenotypic plasticity and the expression of a genetic polymorphism, the major phenotypic leaps that the mechanisms of development can produce based on point mutations, or the morphological stasis that reveals how robust a developmental process can be in front of genetic change. Evolvability is subject itself to evolution, but it is still uncertain to what extent there is positive selection for enhanced evolvability, or for evolvability biased in a specific direction.

The Stillness of Solitude explores the Romantic connections between a selection of seven films from contemporary American filmmakers Sofia Coppola, Wes Anderson, Spike Jonze, and Charlie Kaufman. Linking the current socio-cultural moment, which has been described as ‘metamodern’, to the Romantic era, it describes how the Romantic relation to selfhood, intersubjectivity, and ‘being in the world’ informs the films studied. The first section of the book lays out the aesthetic argument, the second describes the role of imagination and emotion in creating that aesthetic, and the third explores narratives of personal growth and their relation to cultural history. The overall structure of the book traces the progression of Romantic thought and situates the films historically, while simultaneously engaging with an up-to-the-moment present. It explores gender, childhood, the artistic process, revolution, scepticism, the natural world, love, and death through specific discourses of contemporary film theory including aesthetics, cinematic metatextuality, feminist criticism, eco-criticism and animal studies, and ethical studies. It argues for the emergence of a particular strain of American ‘independent’ cinema that draws extensively on 1970s New Hollywood film in ways differing from 1990s ‘smart’ cinema, and considers how the films use both classical Hollywood and American/European arthouse cinema tropes to create an uneasy dialectic between the two, emphasising the anxieties of our own time, nostalgia for an imaginary past, and fear of an uncertain future.

This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.

The Oxford Handbook of Austrian Economics provides an overview of the main methodological, analytical, and practical implications of the Austrian school of economics. This intellectual tradition in economics and political economy has a long history that dates back to Carl Menger in the late nineteenth century. The various contributions discussed in this book all reflect this "tension" of an orthodox argumentative structure (rational choice and invisible hand) to address heterodox problem situations (uncertainty, differential knowledge, ceaseless change).The Austrian economists, from the founders to today, seek to derive the invisible-hand theorem from the rational-choice postulate via institutional analysis in a persistent and consistent manner. The Handbook, which consists of nine parts, and 34 chapters, covers a variety of topics including: methodology, microeconomics (market process theory and spontaneous order), macroeconomics (capital theory and Austrian business cycle theory, and free banking), institutions and organizational theory, political economy, development and social change, and the 2008 financial crisis. The goals of the volume are twofold. First, to introduce readers to some of the main theories and insights of the Austrian school. Second, to demonstrate how Austrian economics provides a set of tools for making original and novel scholarly contributions to the broader economics discipline. By providing insight into the central Austrian theories, the volume will be valuable to those who are unfamiliar with Austrian economics. At the same time, it will be appealing to those already familiar with Austrian economics, given its emphasis on Austrian economics as a live and progressive research program in the social sciences.

This text reviews fundametals and incorporates key themes of quantum physics. One theme contrasts boson condensation and fermion exclusivity. Bose–Einstein condensation is basic to superconductivity, superfluidity and gaseous BEC. Fermion exclusivity leads to compact stars and to atomic structure, and thence to the band structure of metals and semiconductors with applications in material science, modern optics and electronics. A second theme is that a wavefunction at a point, and in particular its phase is unique (ignoring a global phase change). If there are symmetries, conservation laws follow and quantum states which are eigenfunctions of the conserved quantities. By contrast with no particular symmetry topological effects occur such as the Bohm–Aharonov effect: also stable vortex formation in superfluids, superconductors and BEC, all these having quantized circulation of some sort. The quantum Hall effect and quantum spin Hall effect are ab initio topological. A third theme is entanglement: a feature that distinguishes the quantum world from the classical world. This property led Einstein, Podolsky and Rosen to the view that quantum mechanics is an incomplete physical theory. Bell proposed the way that any underlying local hidden variable theory could be, and was experimentally rejected. Powerful tools in quantum optics, including near-term secure communications, rely on entanglement. It was exploited in the the measurement of CP violation in the decay of beauty mesons. A fourth theme is the limitations on measurement precision set by quantum mechanics. These can be circumvented by quantum non-demolition techniques and by squeezing phase space so that the uncertainty is moved to a variable conjugate to that being measured. The boundaries of precision are explored in the measurement of g-2 for the electron, and in the detection of gravitational waves by LIGO; the latter achievement has opened a new window on the Universe. The fifth and last theme is quantum field theory. This is based on local conservation of charges. It reaches its most impressive form in the quantum gauge theories of the strong, electromagnetic and weak interactions, culminating in the discovery of the Higgs. Where particle physics has particles condensed matter has a galaxy of pseudoparticles that exist only in matter and are always in some sense special to particular states of matter. Emergent phenomena in matter are successfully modelled and analysed using quasiparticles and quantum theory. Lessons learned in that way on spontaneous symmetry breaking in superconductivity were the key to constructing a consistent quantum gauge theory of electroweak processes in particle physics.