Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

Tax avoidance as an anti-austerity issue: the progress of a protest issue through the public sphere

Theorists of left and right agree that periods of crisis are fertile times at which to precipitate change. However, protesters on the periphery of the public sphere must overcome barriers, or what Habermas called ‘sluice gates’, if their discourse is to be publicly and politically influential. This study of newspaper discourse and activity in parliament and the public sphere over a six year period takes tax justice campaigning in the UK as a case study, and in particular protest group UK Uncut’s attempt to mobilize opposition to austerity by advocating a crackdown on tax avoidance as an alternative to cuts. It finds that whilst UK Uncut successfully amplified existing arguments previously raised by experts, trade unions and the left-leaning press, austerity barely figured in debate about tax avoidance once it was picked up by other actors in the public sphere on the other side of the 'sluice gates'. The reasons for this were structural and discursive, related to the role and interests of receptive actors at the institutional centre of the public sphere, and their ability, along with the conservative press, to transform the moral framing of tax avoidance from the injustice of making the poor pay for the financial crisis through cuts, into the 'unfairness' of middle class earners paying higher taxes than wealthier individuals and corporations. The latter reifies the 'hardworking taxpayer', and implies a more instrumental and clientalistic relationship to the state, and an essentially neoliberal sense of fairness. Where neoliberal ideology was challenged, it was in social conservative terms – nationalist opposition to globalisation, framing multinational corporations as a threat to the domestic high street – rather than protesters’ social democratic challenge to market power and social injustice. This indicates how a progressive message from the periphery can be co-opted into the currently resurgent right-wing populism

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Law, politics and the governance of English and Scottish joint-stock companies 1600-1850

This article examines the impact of law on corporate governance by means of a case study of joint-stock enterprise in England and Scotland before 1850. Based on a dataset of over 450 company constitutions together with qualitative information on governance practice, it finds little evidence to support the hypothesis that common-law regimes such as England were more supportive of economic growth than civil-law jurisdictions such as Scotland: indeed, levels of shareholder protection were slightly stronger in the civil-law zone. Other factors, such as local political institutions, played a bigger role in shaping organisational forms and business practice

Educating consent? A conversation with Noam Chomsky on the university and business school education

In what follows, we present a conversation with Professor Noam Chomsky on the topic of whether the business school might be a site for progressive political change. The conversation covers a number of key issues related to pedagogy, corporate social responsibility and working conditions in the contemporary business school. We hope the conversion will contribute to the ongoing discussion about the role of the business school in neoliberal societies

Overcoming the Impasse in Modern Economics

This document is the Accepted Manuscript version of the following article: Francesca Gagliardi, and David Gindis, 'Overcoming the Impasse in Modern Economics', Competition and Change, Vol. 15 (4): 336-42, November 2011, doi: 10.1179/102452911X13135903675732. Published by SAGE.Peer reviewe

Elements of Infrastructure Demand in Multiplayer Video Games

With the advent of organized eSports, game streaming, and always-online video games, there exist new and more pronounced demands on players, developers, publishers, spectators, and other video game actors. By identifying and exploring elements of infrastructure in multiplayer games, this paper augments Bowman’s (2018) conceptualization of demands in video games by introducing a new category of ‘infrastructure demand’ of games. This article describes how the infrastructure increasingly built around video games creates demands upon those interacting with these games, either as players, spectators, or facilitators of multiplayer video game play. We follow the method described by Susan Leigh Star (1999), who writes that infrastructure is as mundane as it is a critical part of society and as such is particularly deserving of academic study. When infrastructure works properly it fades from view, but in doing so loses none of its importance to human endeavor. This work therefore helps to make visible the invisible elements of infrastructure present in and around multiplayer video games and explicates the demands these elements create on people interacting with those games

Sociological and Communication-Theoretical Perspectives on the Commercialization of the Sciences

Both self-organization and organization are important for the further development of the sciences: the two dynamics condition and enable each other. Commercial and public considerations can interact and "interpenetrate" in historical organization; different codes of communication are then "recombined." However, self-organization in the symbolically generalized codes of communication can be expected to operate at the global level. The Triple Helix model allows for both a neo-institutional appreciation in terms of historical networks of university-industry-government relations and a neo-evolutionary interpretation in terms of three functions: (i) novelty production, (i) wealth generation, and (iii) political control. Using this model, one can appreciate both subdynamics. The mutual information in three dimensions enables us to measure the trade-off between organization and self-organization as a possible synergy. The question of optimization between commercial and public interests in the different sciences can thus be made empirical.Comment: Science & Education (forthcoming