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This chapter examines the contests between Protestants and Catholics over the filial claims to Saint Augustine’s religious authority, as they played out in the competing translations of the Confessions into English in the 1620s. This was a constituent part of the battle that raged near the end of James I’s reign to establish religious orthodoxy and to maintain state control over it. The Confessions was not a useful polemical tool, but the chapter details the responsive confessional statements of one of its expert readers, John Donne. The two publications that framed his public life were Pseudo‐Martyr (1610) and Devotions upon Emergent Occasions (1624). In them, Donne challenged Augustine’s resolution of a spiritual crisis with a change of church. Donne complied, but only in respect of the body politic. He became an improbable literary spokesperson for the Protestant nation.

This chapter examines the contests between Protestants and Catholics over the filial claims to Saint Augustine’s religious authority, as they played out in the competing translations of the Confessions into English in the 1620s. This was a constituent part of the battle that raged near the end of James I’s reign to establish religious orthodoxy and to maintain state control over it. The Confessions was not a useful polemical tool, but the chapter details the responsive confessional statements of one of its expert readers, John Donne. The two publications that framed his public life were Pseudo-Martyr (1610) and Devotions upon Emergent Occasions (1624). In them, Donne challenged Augustine’s resolution of a spiritual crisis with a change of church. Donne complied, but only in respect of the body politic. He became an improbable literary spokesperson for the Protestant nation.

What are neural network models, what kind of cognitive processes can they perform, and what do they teach us about representations and consciousness? First, this chapter explains the functioning of reduced neuron models. We construct neural networks using these building blocks and explore how they accomplish memory, categorization and other tasks. Computational advantages of parallel-distributed networks are considered, and we explore their emergent properties, such as in pattern completion. Artificial neural networks appear instructive for understanding consciousness, as they illustrate how stable representations can be achieved in dynamic systems. More importantly, they show how low-level processes result in high-level phenomena such as memory retrieval. However, an essential remaining problem is that neural networks do not possess a mechanism specifying what kind of information (e.g. sensory modality) they process. Going back to the classic labeled-lines hypothesis, it is argued that this hypothesis does not offer a solution to the question how the brain differentiates the various sensory inputs it receives into distinct modalities. The brain is observed to live in a "Cuneiform room" by which it only receives and emits spike messages: these are the only source materials by which it can construct modally differentiated experiences.

This chapter focuses in detail on Donne's most neglected text: the Essayes in Divinity (composed c.1614; published 1651) and marks the transition to the second part of the book: having presented a survey of Donne's reading in the first two chapters, the book now moves on to five detailed case studies. The Essayes are a series of Scripture meditations; through reading the first verses of Genesis and Exodus, Donne defines his sense of Christian identity and vocation. The main model of interpretation in the Essayes is Augustine's Confessions, a text which is constantly invoked for doctrinal and devotional guidance. The chapterer traces key Augustinian passages and charts their re-emergence both in the wider sphere of Renaissance devotional writing and in Donne's later career, in the Devotions upon Emergent Occasions (1624) and in his sermons. The Essayes serve as a self-conscious act of religious and interpretive initiation, which prepare for Donne's ordination in 1615.

In summary, this book proposes that improper sites of subtitling and dubbing provide a key to revaluing translation’s role within screen culture broadly. By analysing a range of emergent practices, Speaking in Subtitles has explored ‘errancy’ as a fault line rapidly spreading across the surface of contemporary screen translation, transferring attention away from endless, unresolvable debates on ‘quality’ towards the geopolitics that determine and delimit value systems in the first place. The concrete translation practices explored in this book identify language diversity as a major trajectory within digital and online modes of media engagement. Paying attention to improper sites of subtitling and dubbing provides a crucial key, it argues, to revaluing translation’s role within screen culture broadly—these ‘error screens’ are central, not peripheral, to screen culture as the risks of linguistic and cultural mutation that attend interlingual translation keep films, TV programs and other forms of screen media circulating, evolving and living-on.

This book promotes good risk leadership practices in promoting effective risk-taking throughout the organization. We ascribe this to five essential elements: (1) map the key risks that can affect the firm, (2) decide how to deal with these risks, (3) lay out the execution and monitor progress, (4) discuss and prepare for unexpected circumstances, and (5) build a responsive organization to deal with emergent risks. These efforts involve directors, executives, line-managers, risk specialists and every employee throughout the firm in their designated roles. Dealing with risk is everybody’s business and is commensurate with ‘good management’.

This chapter focuses on hybrid selves, a new kind of self that arises as a result of the distinct context, experiences and set of social and material practices that a person engages in to understand themselves and those around them. While there are many different ways to define hybrid, its use here examines those novel socio-cultural transformations, combinations and “mixings” that take shape at the moment of cultural encounter. Hybrid selves form differently even if facing the same set of circumstances and conditions such studying the everyday lives of business people can elucidate the repertoires of actions that they embody and eschew. By outlining the main tenets of hybrid selves, this chapter challenges conceptualizations of ‘self’ that are based on static notions of identity which limit how we can understand people. It provides examples and comparative illustrations of hybrid selves and contrasts them with research that aims to study similar people in diversity and cross-cultural management. The chapter points out main differences between hybrid selves and bi-cultural or multicultural notions of identity that generally offer hyphenation as a solution to the complex ways people may understand themselves.

This chapter begins by introducing semantic memory as an attractor neural network (ANN). This neural network ‘learns’ and ‘retrieves’ using essentially the Hebbian mechanism, and stores pieces of information as patterns of activation that complete themselves when only a part is externally excited (attractor states). This chapter first follows the line of research initiated by Hopfield in 1982, under the strong influence of developments that have occurred during the 1960s and 1970s in the modern statistical mechanics of fields concerning the so-called Universality of Collective Emergent Behaviours. Then, the author argues that the traditional amorphous, the fully interconnected Hopfield architecture, needs to be replaced by a modular structure for semantic memory. A neural network model is thus proposed in this chapter that aims to provide evidence about the function and dysfunction of semantic memory.

This chapter considers the emergence of human rights in Thailand through the trope of the eventedness of the everyday, arguing that we may profitably understand this emergence as a turning back to the ordinary in novel ways rather than as the global subsuming the local. Approaching human rights as an event in Thai political and moral life allows us to see how they occasion a re-contextualization of debates within Buddhist morality. Social actors have taken human rights to offer an opportunity to challenge conservative views on Buddhism that sustain social stratification with egalitarian visions of Buddhism. This chapter argues, therefore, for an understanding of the specificities of human rights as an emergent event in Thailand to see how their articulation with and through Buddhist morality provided resources not for a transformation of Thai morality, under the model of escape, transcendence or rupture, but for its transfiguration.

This chapter aims to set Donne as a plague writer into the context of the fatal and memorable year and, by doing so, correlate the epidemic crisis in London with a transformative personal crisis for Donne, occasioned by his own near-fatal illness two years before. The central texts include three works not usually taken together but all part of Donne's engagement with disease: his own, London's, and the world's. The Devotions upon Emergent Occasions, published the year before the plague as a meditation on his recent illness, would be in the bookstalls during the plague year, along with the 1625 edition of the “Anniversaries.” These earlier poems, with their prolonged lament for a “sicke World,” would now likely be reread in the light of the plague, and in the context of Donne's writing, as a prelude to the Devotions.

In this dialogue, the Harvard neuroscientist, Alvaro Pascual-Leone initially reflects on the importance of ‘unlearning’ and forgetting. He then gives a detailed explanation of, and how he carries out, transcraneal magnetic stimulation (TMS) and how he uses this technology to fight diseases, as well as explaining his experiments on inattentional blindness. He then discusses how the brain acts as a hypothesis generator and whether the brain, the mind and the soul are different things or not. Later reflect on the questions: Is the mind and what we are a consequence of the brain’s structure?  Do changes in the brain change our reality? And why are a person’s dreams important? Then he explains how freewill and decision-making work from the brain, and relates his vision of intelligence and where it may be generated from, explaining the differences between the mind and the brain. He finally reflects on what is known so far about the brain’s “dark energy” and the way we are continuously being surprised by the wonders of the brain's plasticity.

People have always been fascinated by cooperation and altruism in animals, in part to shed light on our own propensity or reluctance to help others. Darwin’s theory added a certain urgency to the subject because the principle of “nature red in tooth and claw” superficially seems to deny the possibility of altruism and cooperation altogether. Some evolutionary biologists have accepted and even reveled in this vision of nature, giving rise to statements such as “the economy of nature is competitive from beginning to end … scratch an ‘altruist’ and watch a hypocrite bleed”. Others have gone so far in the opposite direction as to proclaim the entire earth a unit that cooperatively regulates its own atmosphere (Lovelock 1979). The truth is somewhere between these two extremes; cooperation and altruism can evolve but only if special conditions are met. As might be expected from the polarized views outlined above, achieving this middle ground has been a difficult process. Science is often portrayed as a heroic march to the truth, but in this case, it is more like the Three Stooges trying to move a piano. I don’t mean to underestimate the progress that been made—the piano has been moved—but we need to appreciate the twists, turns, and reversals in addition to the final location. To see why cooperation and altruism pose a problem for evolutionary theory, consider the evolution of a nonsocial adaptation, such as cryptic coloration. Imagine a population of moths that vary in the degree to which they match their background. Every generation, the most conspicuous moths are detected and eaten by predators while the most cryptic moths survive and reproduce. If offspring resemble their parents, then the average moth will become more cryptic with every generation. Anyone who has beheld a moth that looks exactly like a leaf, right down to the veins and simulated herbivore damage, cannot fail to be impressed by the power of natural selection to evolve breathtaking adaptations at the individual level. Now consider the same process for a social adaptation, such as members of a group warning each other about approaching predators.

Many similar individuals interacting only with their nearest neighbors create through self-organization a new collective entity, with its own laws and properties: swarms of locusts, flocks of birds, schools of fish, herds of antelopes and insect societies. The description of these phenomena in terms of mathematical models from statistical physics has provided the basis for our present understanding.

Simple or ‘complicated’ systems that display linear, predictable behaviours are contrasted with complex systems characterised by ‘emergent properties’ arising from inter-relationships between parts, feedback loops and sometimes unexpected effects arising from changes in other parts of the system. Particular characteristics of complex social systems and learning from change processes in complex social systems are described. The relevance of systems thinking to epidemiology, public health research and ‘evidence’ is noted including the importance of recognising wider context, the relationship between knowledge and uncertainty and development of new methodologies. It is noted that approaches to public health such as socio-ecological, community development and policy, regulation and ‘whole organisation’ approaches show some features of systemic approaches but that this is not always explicit or consistent. Opportunities and barriers to further development of systemic approaches to public health in the new organisational arrangements for public health in England are highlighted.

The source function for scattering, (2.32), is more complicated than a thermal source function on two accounts: it is not a function of local conditions alone, but involves conditions throughout the atmosphere, through the local radiation field, and the phase function, Pij(s, d), may be an extremely complex function of the directions, s and d, and the states of polarization, i and j. The general solution, (2.87), is still valid, but it is now an integral equation, involving the intensity both on the left-hand side and under the integral on the right-hand side. Successive approximations, starting with the first-order scattering term [third term on the right-hand side of (2.116)], are an obvious approach, and would lead to a solution, but there are more efficient and more accurate ways to solve the problem. Many methods are available because their fundamental theory has proved to be mathematically interesting and because there are important applications in neutron diffusion theory and astrophysics. These motivations are extraneous to atmospheric science, but the availability of the methodology has led to its adoption and extension to atmospheric problems. Many methods are available because their fundamental theory has proved to be mathematically interesting and because there are important applications in neutron diffusion theory and astrophysics. These motivations are extraneous to atmospheric science, but the availability of the methodology has led to its adoption and extension to atmospheric problems. Solutions to scattering problems can be elaborate and mathematically elegant; they can also be numerically onerous but, with access to modern computers, “exact” solutions are feasible, given the input parameters τv, av (=sv/ev), and Pi j. For monochromatic calculations with simple phase functions, numerical solutions present few difficulties. Nevertheless, the combination of unfamiliar formalism with inaccessible and undocumented algorithms can be daunting for those with only a peripheral interest in radiation calculations. It is, therefore, relevant to note that available data are imprecise and virtually never require the accuracy available from exact methods. Easily visualized two-stream approximations, combined with similarity relations to handle complex phase functions (see §§8.4.4 and 8.5.6), are often more than adequate, and some angular information can be added, if required, from the use of Eddington’s second approximation (§ 2.4.5).

This chapter discusses the emerging risk landscape and considers the value creating potential of uncertainty. Assuming a strategic decision-making perspective it is argued that risk management practices can help firms make better decisions for better risk-return outcomes. Effects of imperfect information and cognitive biases are considered, as are different ways to avoid them. It is shown how systematic risk analysis can help reduce information distortion and how these techniques can challenge forward looking strategic plans as well as ongoing project investments. The human limitations in making advance predictions under uncertainty are illustrated by predictable surprises. It is discussed how the value of effective risk management derive from dynamic processes that shield against downside losses and pursue prudent strategic risk-taking for upside potential. The role of innovation in developing upside gain is discussed as is the individual human involvement in these value creating activities. (143 words)

Organisms that developed independent existence had to rely on external sources of energy and, in plants, photosynthesis was probably the end of an evolutionary process that saw cells free living in sunlight. How did this process evolve? The earliest organisms thought to be bacteria are potentially detectable 3.5 billion years ago. Photosynthesis commenced about 3 billion years ago or so with oxygen-producing organisms in abundance 2.7 billion years ago. Fossil stromatolites, 2.2 billion years old, and containing blue-green algae and bacteria are well established, and, astonishingly, are very similar to present-day stromatolites. What provided the impetus for early molecular primordia to eventually generate a living cell? A robust energy supply is undoubtedly essential. Early chemical reactions would have to be coupled directly or indirectly to it. Providing the energy supply is sustained, Prigogine’s dissipative mechanism, seeing order derive increasingly from continued energy flow, is the crucial underpin. The early molecular components would have to be connected to form an integrated, holistic system of low entropy and information flow between them. With increasing experimentation, a stabilizing hierarchical structure would come to dominate, initially, between molecules, then groups of molecules as modules. This early system had to become teleonomic; that is being purposive in maintenance and replication. Negative feedback would have helped stabilize the early structure by keeping the internal environment constant, but may have evolved to counteract destabilizing noise. Each of these criteria is discussed in this chapter to try and provide understanding of this vital event.

At first blush, computing and biology seem an odd couple, yet they formed a liaison of sorts from the very first years of the electronic digital computer. Following a seminal paper published in 1943 by neurophysiologist Warren McCulloch and mathematical logician Warren Pitts on a mathematical model of neuronal activity, John von Neumann of the Institute of Advanced Study, Princeton, presented at a symposium in 1948 a paper that compared the behaviors of computer circuits and neuronal circuits in the brain. The resulting publication was the fountainhead of what came to be called cellular automata in the 1960s. Von Neumann’s insight was the parallel between the abstraction of biological neurons (nerve cells) as natural binary (on–off) switches and the abstraction of physical computer circuit elements (at the time, relays and vacuum tubes) as artificial binary switches. His ambition was to unify the two and construct a formal universal theory.One remarkable aspect of von Neumann’s program was inspired by the biology: His universal automata must be able to self-reproduce. So his neuron-like automata must be both computational and constructive. In 1955, invited by Yale University to deliver the Silliman Lectures for 1956, von Neumann chose as his topic the relationship between the computer and the brain. He died before being able to deliver the lectures, but the unfinished manuscript was published by Yale University Press under the title The Computer and the Brain (1958). Von Neumann’s definitive writings on self-reproducing cellular automata, edited by his one-time collaborator Arthur Burks of the University of Michigan, was eventually published in 1966 as the book Theory of Self-Reproducing Automata. A possible structure of a von Neumann–style cellular automaton is depicted in Figure 7.1. It comprises a (finite or infinite) configuration of cells in which a cell can be in one of a finite set of states. The state of a cell at any time t is determined by its own state and those of its immediate neighbors in the preceding point of time t – 1, according to a state transition rule.

A major concern to the founders of modern logic—Frege, Peirce, Russell, and Hilbert—was to give an account of the logical structure of valid reasoning. Taking valid reasoning in mathematics as paradigmatic, these pioneers led the way in developing the accounts of logic which we teach today and that underwrite the work in model theory, proof theory, and definability theory. The resulting notions of proof, model, formal system, soundness, and completeness are things that no one claiming familiarity with logic can fail to understand, and they have also played an enormous role in the revolution known as computer science. The success of this model of inference led to an explosion of results and applications. But it also led most logicians—and those computer scientists most influenced by the logic tradition—to neglect forms of reasoning that did not fit well within this model. We are thinking, of course, of reasoning that uses devices like diagrams, graphs, charts, frames, nets, maps, and pictures. The attitude of the traditional logician to these forms of representation is evident in the quotation of Neil Tennant in Chapter I, which expresses the standard view of the role of diagrams in geometrical proofs. One aim of our work, as explained there, is to demonstrate that this dogma is misguided. We believe that many of the problems people have putting their knowledge of logic to work, whether in machines or in their own lives, stems from the logocentricity that has pervaded its study for the past hundred years. Recently, some researchers outside the logic tradition have explored uses of diagrams in knowledge representation and automated reasoning, finding inspiration in the work of Euler, Venn, and especially C. S. Peirce. This volume is a testament to this resurgence of interest in nonlinguistic representations in reasoning. While we applaud this resurgence, the aim of this chapter is to strike a cautionary note or two. Enchanted by the potential of nonlinguistic representations, it is all too easy to overreact and so to repeat the errors of the past.