Regularization and complexity control in feed-forward networks

In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets

We study the dynamics of on-line learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained by computer simulations.

A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Creating and managing value in collaborative networks

This is a theoretical paper that examines the interplay between individual and collective capabilities and competencies and value transactions in collaborative environments. The theory behind value creation is examined and two types of value are identified, internal value (Shareholder value) and external value (Value proposition). The literature on collaborative enterprises/network is also examined with particular emphasis on supply chains, extended/virtual enterprises and clusters as representatives of different forms and maturities of collaboration. The interplay of value transactions and competencies and capabilities are examined and discussed in detail. Finally, a model is presented which consists of value transactions and a table which compares the characteristics of different types of collaborative enterprises/networks. It is proposed that this model presents a platform for further research to develop an in-depth understanding into how value may be created and managed in collaborative enterprises/networks.

Phase transitions and memory effects in the dynamics of Boolean networks

The generating functional method is employed to investigate the synchronous dynamics of Boolean networks, providing an exact result for the system dynamics via a set of macroscopic order parameters. The topology of the networks studied and its constituent Boolean functions represent the system's quenched disorder and are sampled from a given distribution. The framework accommodates a variety of topologies and Boolean function distributions and can be used to study both the noisy and noiseless regimes; it enables one to calculate correlation functions at different times that are inaccessible via commonly used approximations. It is also used to determine conditions for the annealed approximation to be valid, explore phases of the system under different levels of noise and obtain results for models with strong memory effects, where existing approximations break down. Links between Boolean networks and general Boolean formulas are identified and results common to both system types are highlighted. © 2012 Copyright Taylor and Francis Group, LLC.

The interplay between microscopic and mesoscopic structures in complex networks

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database. © 2011 Reichardt et al.

Human resource management, employee attitudes and business performance in Post Office Ltd

Whilst a consistent link between the adoption of human resource management (HRM) practices by organisations and their performance has been confirmed by numerous studies, there is a need for greater understanding of why such effects occur. Recently, the attention of researchers has shifted towards understanding the so-called ‘black box’ linking HRM and business performance. This study focuses on this area of research by testing processes through which HRM may affect performance, in particular the process of HR implementation, mediation mechanisms, and fit with internal and external boundary conditions. This research was based on a sample of 136 Post Office branches in the UK and investigated the role of HR implementation, employee attitudes and competitive environment. The study revealed that HR implementation, a climate for service, job satisfaction and effective organisational commitment predicted independent measures of economic and service performance in branches. Employee attitudes moderated the relationship between implemented HRM and service performance, and both job satisfaction and commitment were found to mediate relationships between a climate for service and service performance. Finally, relative levels of competition faced by branches moderated the relationship between employee attitudes and sales. The findings demonstrate how the process of HR implementation, interactions with employee attitudes and moderation by external competition all influence the impact of HR systems on service and economic performance outcomes. These results illustrate the need for greater attention to processes of internal and external fit within HRM research in order to develop theory relating to why HR systems affect performance.

Human-computer interaction and innovation in handheld, mobile and wearable technologies

Handheld and mobile technologies have witnessed significant advances in functionality, leading to their widespread use as both business and social networking tools. Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies reviews concepts relating to the design, development, evaluation, and application of mobile technologies. Studies on mobile user interfaces, mobile learning, and mobile commerce contribute to the growing body of knowledge on this expanding discipline.

Multiplexers and demultiplexers based on fibre Bragg gratings and optical circulators for DWDM systems

Two different architectures of multiplexers/demultiplexers based on 4×1 and 1×4 configurations are discussed. These architectures are implemented using apodized fibre Bragg gratings as optical filters and optical circulators. The spectral characteristics of the devices for channel separations of 100 GHz and 50 GHz are analysed and their performance is evaluated. Optical switch and cross-connect configurations are also demonstrated.

Spillovers from FDI and local networks:the importance of transactional linkages and vertical keiretsu in Japan

Purpose – The purpose of this paper is to explore the importance of host country networks and organisation of production in the context of international technology transfer that accompanies foreign direct investment (FDI). Design/methodology/approach – The empirical analysis is based on unbalanced panel data covering Japanese firms active in two-digit manufacturing sectors over a seven-year period. Given the self-selection problem affecting past sectoral-level studies, using firm-level panel data is a prerequisite to provide robust empirical evidence. Findings – While Japan is thought of as being a technologically advanced country, the results show that vertical productivity spillovers from FDI occur in Japan, but they are sensitive to technological differences between domestic firms and the idiosyncratic Japanese institutional network. FDI in vertically organised keiretsu sectors generates inter-industry spillovers through backward and forward linkages, while FDI within sectors linked to vertical keiretsu activities adversely affects domestic productivity. Overall, our results suggest that the role of vertical keiretsu is more prevalent than that of horizontal keiretsu. Originality/value – Japan’s industrial landscape has been dominated by institutional clusters or networks of inter-firm organisations through reciprocated, direct and indirect ties. However, interactions between inward investors and such institutionalised networks in the host economy are seldom explored. The role and characteristics of local business groups, in the form of keiretsu networks, have been investigated to determine the scale and scope of spillovers from inward FDI to Japanese establishments. This conceptualisation depends on the institutional mechanism and the market structure through which host economies absorb and exploit FDI.

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

Privacy and the City:user identification and location semantics in location-based social networks

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user’s location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker’s goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the “Residence” category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users’ population that determines the complexity of the identification task, rather than the individual behaviour.

Scaling and Multiscaling Exponents in Networks and Flows

The main focus of this paper is on mathematical theory and methods which have a direct bearing on problems involving multiscale phenomena. Modern technology is refining measurement and data collection to spatio-temporal scales on which observed geophysical phenomena are displayed as intrinsically highly variable and intermittant heirarchical structures,e.g. rainfall, turbulence, etc. The heirarchical structure is reflected in the occurence of a natural separation of scales which collectively manifest at some basic unit scale. Thus proper data analysis and inference require a mathematical framework which couples the variability over multiple decades of scale in which basic theoretical benchmarks can be identified and calculated. This continues the main theme of the research in this area of applied probability over the past twenty years.

Understanding inter-firm networks and types of innovation in SMEs:a social network perspective

Innovation is one of the key drivers for gaining competitive advantages in any firms. Understanding knowledge transfer through inter-firm networks and its effects on types of innovation in SMEs is very important in improving SMEs innovation. This study examines relationships between characteristics of inter-firm knowledge transfer networks and types of innovation in SMEs. To achieve this, social network perspective is adopted to understand inter-firm knowledge transfer networks and its impact on innovation by investigating how and to what extend ego network characteristics are affecting types of innovation. Therefore, managers can develop the firms'network according to their strategies and requirements. First, a conceptual model and research hypotheses are proposed to establish the possible relationship between network properties and types of innovation. Three aspects of ego network are identified and adopted for hypotheses development: 1) structural properties which address the potential for resources and the context for the flow of resources, 2) relational properties which reflect the quality of resource flows, and 3) nodal properties which are about quality and variety of resources and capabilities of the ego partners. A questionnaire has been designed based on the hypotheses. Second, semistructured interviews with managers of five SMEs have been carried out, and a thematic qualitative analysis of these interviews has been performed. The interviews helped to revise the questionnaire and provided preliminary evidence to support the hypotheses. Insights from the preliminary investigation also helped to develop research plan for the next stage of this research.