Learning in neural networks and stochastic approximation methods with averaging

The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure is to be performed as fast as possible and in a simple computational fashion, the two requirements which are usually not satisfied practically by the methods developed so far. Moreover, the presence of random inaccuracies are usually not taken into account. In view of these three issues, an alternative stochastic approximation approach discussed in the paper, seems to be very promising.

Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis

Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements..

Social networks and positive and negative affect

We followed 100 university students in the UK for one week, instructing them to record all face-to-face, phone and digital contacts during the day as well as their positive and negative affect. We wanted to see how positive and negative affect spread around a social network while taking into account participants’ socio-demographic data, personality, general health and gratitude scores. We focused on the participants’ connections with those in their class; excluding friends and family outside this group. The data was analysed using actor-based models implemented in SIENA. Results show differences between positive and negative affect dynamics in this environment and an influence of personality traits on the average number and rate of communication.

Fora, networks and public examinations: the role and significance of public participation in the new regional plan for South East England

This paper overviews the main conceptual frameworks for understanding participatory approaches to land use planning and explores their utility in analysing the experience of a recent regional planning exercise in South East England. In particular it examines the contribution of recent ‘New Institutionalist’ ideas to our understanding of participatory processes and the implications for practice of using them to build strategies of public involvement in policy-making and implementation.

Social networks and occupational choice: the endogenous formation of attitudes and beliefs about tax compliance

The paper analyses the emergence of group-specific attitudes and beliefs about tax compliance when individuals interact in a social network. It develops a model in which taxpayers possess a range of individual characteristics – including attitude to risk, potential for success in self-employment, and the weight attached to the social custom for honesty – and make an occupational choice based on these characteristics. Occupations differ in the possibility for evading tax. The social network determines which taxpayers are linked, and information about auditing and compliance is transmitted at meetings between linked taxpayers. Using agent-based simulations, the analysis demonstrates how attitudes and beliefs endogenously emerge that differ across sub-groups of the population. Compliance behaviour is different across occupational groups, and this is reinforced by the development of group-specific attitudes and beliefs. Taxpayers self-select into occupations according to the degree of risk aversion, the subjective probability of audit is sustained above the objective probability, and the weight attached to the social custom differs across occupations. These factors combine to lead to compliance levels that differ across occupations.

Moving the nanoscience and technology (NST) debate forwards: short-term impacts, long-term uncertainty and the social constitution

Nanoscience and technology (NST) are widely cited to be the defining technology for the 21st century. In recent years, the debate surrounding NST has become increasingly public, with much of this interest stemming from two radically opposing long-term visions of a NST-enabled future: ‘nano-optimism’ and ‘nano-pessimism’. This paper demonstrates that NST is a complex and wide-ranging discipline, the future of which is characterised by uncertainty. It argues that consideration of the present-day issues surrounding NST is essential if the public debate is to move forwards. In particular, the social constitution of an emerging technology is crucial if any meaningful discussion surrounding costs and benefits is to be realised. An exploration of the social constitution of NST raises a number of issues, of which unintended consequences and the interests of those who own and control new technologies are highlighted.

Primary evolving networks and the comparative analysis of robust and fragile structures

In this paper we consider the structure of dynamically evolving networks modelling information and activity moving across a large set of vertices. We adopt the communicability concept that generalizes that of centrality which is defined for static networks. We define the primary network structure within the whole as comprising of the most influential vertices (both as senders and receivers of dynamically sequenced activity). We present a methodology based on successive vertex knockouts, up to a very small fraction of the whole primary network,that can characterize the nature of the primary network as being either relatively robust and lattice-like (with redundancies built in) or relatively fragile and tree-like (with sensitivities and few redundancies). We apply these ideas to the analysis of evolving networks derived from fMRI scans of resting human brains. We show that the estimation of performance parameters via the structure tests of the corresponding primary networks is subject to less variability than that observed across a very large population of such scans. Hence the differences within the population are significant.

Globalization and technology-mediated distance education: developing countries’ perspective

The contemporary global economy places great value on highly educated workers but devalues workers in repetitive or low skill jobs. In order to thrive in this new economy, countries must ensure sufficient higher education opportunities for their population. However, a lack of resources is a major barrier faced by many developing countries in expanding their higher education systems. Technology-mediated distance education has the potential to be an invaluable tool in offering educational opportunities to people, if the other necessary conditions for participation are met. Although technology-mediated education was first considered to be a medium to bridge the learning divide across space, today it is feared that it could well become an inequality intensifier. Drawing on examples from developing countries, this paper considers factors regarding implementing technology-mediated distance education, including failure to address contextual issues and possible consequences. Challenges and policy implications are also discussed.

Interregional migration 'wage premia': the case of creative and science and technology graduates in the UK

We analyze the migration behavior of graduates from UK universities with a focus on the salary benefits they receive from the migration process. We focus on sequential interregional migration and specifically examine the case of Science, Technology, Engineering and Mathematics (STEM) and Creative subject graduates. Our analysis differs from previous studies in that it accounts explicitly for migrant selectivity through propensity score matching, and it also classifies graduates into different migration behavior categories. Graduates were classified according to their sequential migration behavior first from their pre-university domicile to university and then from university to first job post-graduation. Our results show that ‘repeat migration’, as expected, is associated with the highest wage premium (around 15%). Other migration behaviors are also advantageous although this varies across different types of graduates. Creative graduates, for instance, do not benefit much from migration behaviors other than repeat migration. STEM graduates, on the contrary, benefit from both late migration and staying in the university area to work.