Moral panic and social theory: Beyond the heuristic

Copyright @ 2011 by International Sociological Association.Critcher has recently conceptualized moral panic as a heuristic device, or 'ideal type'. While he argues that one still has to look beyond the heuristic, despite a few exceptional studies there has been little utilization of recent developments in social theory in order to look 'beyond moral panic'. Explicating two current critical contributions - the first, drawing from the sociologies of governance and risk; the second, from the process/figurational sociology of Norbert Elias - this article highlights the necessity for the continuous theoretical development of the moral panic concept and illustrates how such development is essential to overcome some of the substantial problems with moral panic research: normativity, temporality and (un) intentionality

Pair production of Dirac particles in a d+1-dimensional noncommutative space-time

This work addresses the computation of the propability of fermionic particle pair production in $(d+1)-$ dimensional noncommutative Moyal space. Using the Seiberg-Witten maps that establish relations between noncommutative and commutative field variables, to first order in the noncommutative parameter $\theta$, we derive the probability density of vacuum-vacuum pair production of Dirac particles. The cases of constant electromagnetic, alternating time-dependent and space-dependent electric fields are considered and discussed.Comment: 12 page

Development and Optimization of Gas Diffusion Electrodes for Electrochemical CO2 Reduction at High Current Density

The electrochemical reduction of CO2 to valuable compounds is a promising approach for its substantial utilization and the storage of electricity in chemical form. At present, the main challenges impeding technical realization are i) low production rates due to mass transport limitations deriving from the low solubility of CO2 in the electrolyte, ii) high overpotentials and poor energetic efficiency, necessitating development of more active catalysts, as well as iii) the demonstration of its continuous production which combines the above with low ohmic losses and long-term stability. Furthermore, hydrogen evolution occurs in the same potential range, and becomes dominating at current densities above 10 mA∙cm 2 on conventional metallic electrodes when diffusion of CO2 to the electrode surface becomes rate-determining

Transferring Electrochemical CO2 Reduction from Semi-Batch into Continuous Operation Mode Using Gas Diffusion Electrodes

The electrochemical reduction of C02 is a promising method for its conversion which still suffers from important challenges that have to be solved before indus­ trial realization becomes attractive. The optimization of gas diffusion electrodes is described with respect to catalyst dispersion and mass transport limitations allowing solubility issues to be circumvented and current densities to be increased to industrially relevant values. The transfer of the promising results from semi-batch experiments into continuous mode of operation is demonstrated, and it is indi­cated how the energetic efficiency can be significantly improved by the choice of electrolyte, in terms of concentration and type. Thereby ohmic losses can be decreased and the intrinsic activity be improved

Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices

Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a novel Bayesian framework based on modeling the correlations as products of unit vectors. By specifying a wide range of distributions on a sphere (e.g. the squared-Dirichlet distribution), the proposed approach induces flexible prior distributions for covariance matrices (that go beyond the commonly used inverse-Wishart prior). For modeling real-life spatio-temporal processes with complex dependence structures, we extend our method to dynamic cases and introduce unit-vector Gaussian process priors in order to capture the evolution of correlation among components of a multivariate time series. To handle the intractability of the resulting posterior, we introduce the adaptive $\Delta$-Spherical Hamiltonian Monte Carlo. We demonstrate the validity and flexibility of our proposed framework in a simulation study of periodic processes and an analysis of rat's local field potential activity in a complex sequence memory task.Comment: 49 pages, 15 figure

Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method

It is well established that temporal organization is critical to memory, and that the ability to temporally organize information is fundamental to many perceptual, cognitive, and motor processes. While our understanding of how the brain processes the spatial context of memories has advanced considerably, our understanding of their temporal organization lags far behind. In this paper, we propose a new approach for elucidating the neural basis of complex behaviors and temporal organization of memories. More specifically, we focus on neural decoding - the prediction of behavioral or experimental conditions based on observed neural data. In general, this is a challenging classification problem, which is of immense interest in neuroscience. Our goal is to develop a new framework that not only improves the overall accuracy of decoding, but also provides a clear latent representation of the decoding process. To accomplish this, our approach uses a Variational Auto-encoder (VAE) model with a diversity-encouraging prior based on determinantal point processes (DPP) to improve latent representation learning by avoiding redundancy in the latent space. We apply our method to data collected from a novel rat experiment that involves presenting repeated sequences of odors at a single port and testing the rats' ability to identify each odor. We show that our method leads to substantially higher accuracy rate for neural decoding and allows to discover novel biological phenomena by providing a clear latent representation of the decoding process

The United Kingdom and British Empire: A Figurational Approach

Drawing upon the work of Norbert Elias and the process [figurational] sociology perspective, this article examines how state formation processes are related to, and, affected by, expanding and declining chains of international interdependence. In contrast to civic and ethnic conceptions, this approach focuses on the emergence of the nation/nation-state as grounded in broader processes of historical and social development. In doing so, state formation processes within the United Kingdom are related to the expansion and decline of the British Empire. That is, by focusing on the functional dynamics that are embedded in collective groups, one is able to consider how the UK’s ‘state’ and ‘imperial’ figurations were interdependently related to changes in both the UK and the former British Empire. Consequently, by locating contemporary UK relations in the historical context of former imperial relationships, nationalism studies can go ‘beyond’ the nation/nation-state in order to include broader processes of imperial expansion and decline. Here, the relationship between empire and nationalism can offer a valuable insight into contemporary political movements, especially within former imperial groups