Child As Metaphor: Colonialism, Psy-Governance, and Epistemicide

This paper mobilizes transdisciplinary inquiry to explore and deconstruct the often-used comparison of racialized/colonized people, intellectually disabled people and mad people as being like children. To be childlike is a metaphor that is used to denigrate, to classify as irrational and incompetent, to dismiss as not being knowledge holders, to justify governance and action on others’ behalf, to deem as being animistic, as undeveloped, underdeveloped or wrongly developed, and, hence, to subjugate. We explore the political work done by the metaphorical appeal to childhood, and particularly the centrality of the metaphor of childhood to legitimizing colonialism and white supremacy. The article attends to the ways in which this metaphor contributes to the shaping of the material and discursive realities of racialized and colonized others, as well as those who have been psychiatrized and deemed “intellectually disabled”. Further, we explore specific metaphors of child-colony, and child-mad-“crip”. We then detail the developmental logic underlying the historical and continued use of the metaphorics of childhood, and explore how this makes possible an infantilization of colonized peoples and the global South more widely. The material and discursive impact of this metaphor on children’s lives, and particularly children who are racialized, colonized, and/or deemed mad or “crip”, is then considered. We argue that complex adult-child relations, sane-mad relations and Western-majority world relations within global psychiatry, are situated firmly within pejorative notions of what it means to be childlike, and reproduce multi-systemic forms of oppression that, ostensibly in their “best interests”, govern children and all those deemed childlike

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems

Constraining the Inclination of Binary Mergers from Gravitational-wave Observations

Much of the information we hope to extract from the gravitational-waves signatures of compact binaries is only obtainable when we can accurately constrain the inclination of the source. In this paper, we discuss in detail a degeneracy between the measurement of the binary distance and inclination which limits our ability to accurately measure the inclination using gravitational waves alone. This degeneracy is exacerbated by the expected distribution of events in the universe, which leads us to prefer face-on systems at a greater distance. We use a simplified model that only considers the binary distance and orientation, and show that this gives comparable results to the full parameter estimates obtained from the binary neutron star merger GW170817. For the advanced LIGO-Virgo network, it is only signals which are close to edge-on, with an inclination greater than $\sim 75^{\circ}$ that will be distinguishable from face-on systems. For extended networks which have good sensitivity to both gravitational wave polarizations, for face-on systems we will only be able to constrain the inclination of a signal with SNR 20 to be $45^{\circ}$ or less, and even for loud signals, with SNR of 100, the inclination of a face-on signal will only be constrained to $30^{\circ}$. For black hole mergers observed at cosmological distances, in the absence of higher modes or orbital precession, the strong degeneracy between inclination and distance dominates the uncertainty in measurement of redshift and hence the masses of the black holes

Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks

The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks

Fluorescent carbon dioxide indicators

Over the last decade, fluorescence has become the dominant tool in biotechnology and medical imaging. These exciting advances have been underpinned by the advances in time-resolved techniques and instrumentation, probe design, chemical / biochemical sensing, coupled with our furthered knowledge in biology. Complementary volumes 9 and 10, Advanced Concepts of Fluorescence Sensing: Small Molecule Sensing and Advanced Concepts of Fluorescence Sensing: Macromolecular Sensing, aim to summarize the current state of the art in fluorescent sensing. For this reason, Drs. Geddes and Lakowicz have invited chapters, encompassing a broad range of fluorescence sensing techniques. Some chapters deal with small molecule sensors, such as for anions, cations, and CO2, while others summarize recent advances in protein-based and macromolecular sensors. The Editors have, however, not included DNA or RNA based sensing in this volume, as this were reviewed in Volume 7 and is to be the subject of a more detailed volume in the near future