The making of a new working class? A study of collective actions of migrant workers in South China

In this study, we argue that the specific process of the proletarianization of Chinese migrant workers contributes to the recent rise of labour protests. Most of the collective actions involve workers' conflict with management at the point of production, while simultaneously entailing labour organizing in dormitories and communities. The type of living space, including workers' dormitories and migrant communities, facilitates collective actions organized not only on bases of locality, ethnicity, gender and peer alliance in a single workplace, but also on attempts to nurture workers' solidarity in a broader sense of a labour oppositional force moving beyond exclusive networks and ties, sometimes even involving cross-factory strike tactics. These collective actions are mostly interest-based, accompanied by a strong anti-foreign capital sentiment and a discourse of workers' rights. By providing detailed cases of workers' strikes in 2004 and 2007, we suggest that the making of a new working class is increasingly conscious of and participating in interest-based or class-oriented labour protests

Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed

Analysis of large scale linear programming problems with embedded network structures: Detection and solution algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Linear programming (LP) models that contain a (substantial) network structure frequently arise in many real life applications. In this thesis, we investigate two main questions; i) how an embedded network structure can be detected, ii) how the network structure can be exploited to create improved sparse simplex solution algorithms. In order to extract an embedded pure network structure from a general LP problem we develop two new heuristics. The first heuristic is an alternative multi-stage generalised upper bounds (GUB) based approach which finds as many GUB subsets as possible. In order to identify a GUB subset two different approaches are introduced; the first is based on the notion of Markowitz merit count and the second exploits an independent set in the corresponding graph. The second heuristic is based on the generalised signed graph of the coefficient matrix. This heuristic determines whether the given LP problem is an entirely pure network; this is in contrast to all previously known heuristics. Using generalised signed graphs, we prove that the problem of detecting the maximum size embedded network structure within an LP problem is NP-hard. The two detection algorithms perform very well computationally and make positive contributions to the known body of results for the embedded network detection. For computational solution a decomposition based approach is presented which solves a network problem with side constraints. In this approach, the original coefficient matrix is partitioned into the network and the non-network parts. For the partitioned problem, we investigate two alternative decomposition techniques namely, Lagrangean relaxation and Benders decomposition. Active variables identified by these procedures are then used to create an advanced basis for the original problem. The computational results of applying these techniques to a selection of Netlib models are encouraging. The development and computational investigation of this solution algorithm constitute further contribution made by the research reported in this thesis.This study is funded by the Turkish Educational Council and Mugla University

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Feedback information and the reward positivity

The reward positivity is a component of the event-related brain potential (ERP) sensitive to neural mechanisms of reward processing. Multiple studies have demonstrated that reward positivity amplitude indices a reward prediction error signal that is fundamental to theories of reinforcement learning. However, whether this ERP component is also sensitive to richer forms of performance information important for supervised learning is less clear. To investigate this question, we recorded the electroencephalogram from participants engaged in a time estimation task in which the type of error information conveyed by feedback stimuli was systematically varied across conditions. Consistent with our predictions, we found that reward positivity amplitude decreased in relation to increasing information content of the feedback, and that reward positivity amplitude was unrelated to trial-to-trial behavioral adjustments in task performance. By contrast, a series of exploratory analyses revealed frontal-central and posterior ERP components immediately following the reward positivity that related to these processes. Taken in the context of the wider literature, these results suggest that the reward positivity is produced by a neural mechanism that motivates task performance, whereas the later ERP components apply the feedback information according to principles of supervised learning

Fast micro and slow macro: can aggregation explain the persistence of inflation?

An aggregation exercise is proposed that aims at investigating whether the fast average adjustment of the disaggregate inflation series of the euro area CPI translates into the slow adjustment of euro area aggregate inflation. We first estimate a dynamic factor model for 404 inflation sub-indices of the euro area CPI. This allows to decompose the dynamics of inflation sub-indices in two parts: one due to a commonInflation (Finance) ; Consumer price indexes ; Euro

A multicenter, randomized, controlled study of Training Executive, Attention, and Motor Skills (TEAMS) in Danish preschool children with attention-deficit/hyperactivity disorder: Rationale and description of the intervention and study protocol

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder that is often detected during the preschool years. Neuroimaging data indicate that children with ADHD have brains that are characterized by growth and functional anomalies. Data suggest that the diminution of ADHD symptoms is correlated with improved neural functioning and growth. On the basis of these findings, interventions that target neural growth, which indicates neural development, can possibly lead to a more enduring treatment for ADHD. Training Executive, Attention, and Motor Skills (TEAMS) is a non-pharmacological neurocognitive intervention program that targets preschool children with ADHD. The program is designed to stimulate neurocognitive growth through physical activity and play in combination with psychoeducation and guidance for the parents.Population: Children between the ages of three and six years from Region Zealand in Denmark who have been diagnosed with ADHD are offered participation in the trial. According to a calculation of the strength needed to result in a statistically significant outcome, the estimated group size should be, at minimum, 87 children. On the basis of Region Zealand’s visitation history records, the cohort is expected to include approximately 100 to 120 children.Method: The intervention groups participate in eight weekly group sessions that consist of separate parent and children’s groups. The control groups receive the standard treatment program as outlined by the clinical guidelines of Region Zealand. The ADHD Rating Scale-IV and the Danish version of the Strengths and Difficulties Questionnaire are used to assess ADHD symptom severity before and after the intervention and to monitor the duration of the outcome. A comparative analysis of data from the intervention and control groups will illustrate the study’s results.Study aim: This is a multicenter, randomized, controlled, single-blind, parallel-group study with the primary aims of testing the TEAMS concept and investigating whether the intervention significantly lowers ADHD symptoms and increases the functionality level after the intervention as compared with the control group. A secondary aim is to monitor the duration and endurance of the outcome for six months after the intervention. This study is currently in progress. Full results and conclusions will be reported after the study’s completion in 2015

Mental imagery of positive and neutral memories : a fMRI study comparing field perspective imagery to observer perspective imagery

Imagery perspective can influence what information is recalled, processing style, and emotionality; however, the understanding of possible mechanisms mediating these observed differences is still limited. We aimed to examine differences between memory recall from a field perspective and observer perspective at the neurobiological level, in order to improve our understanding of what is underlying the observed differences at the behavioral level. We conducted a fMRI study in healthy individuals, comparing imagery perspectives during recall of neutral and positive autobiographical memories. Behavioral results revealed field perspective imagery of positive memories, as compared to observer perspective, to be associated with more positive feelings afterwards. At the neurobiological level, contrasting observer perspective to field perspective imagery was associated with greater activity, or less decrease relative to the control visual search task, in the right precuneus and in the right temporoparietal junction (TPJ). Greater activity in the right TPJ during an observer perspective as compared to field perspective could reflect performing a greater shift of perspective and mental state during observer perspective imagery than field perspective imagery. Differential activity in the precuneus may reflect that during observer perspective imagery individuals are more likely to engage in (self-) evaluative processing and visuospatial processing. Our findings contribute to a growing understanding of how imagery perspective can influence the type of information that is recalled and the intensity of the emotional response. Observer perspective imagery may not automatically reduce emotional intensity but this could depend on how the imagined situation is evaluated in relation to the self-concept. (C) 2016 Elsevier Inc. All rights reserved