Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

A meta-heuristic approach to parallel code generation

The efficient generation of parallel code for multi-processor environments, is a large and complicated issue. Attempts to address this problem have always resulted in significant input from users. Because of constraints on user knowledge and time, the automation of the process is a promising and practically important research area. In recent years heuristic approaches have been used to capture available knowledge and make it available for the parallelisation process. Here, the introduction of a novel approach of neural network techniques is combined with an expert system technique to enhance the availability of knowledge to aid in the automatic generation of parallel code.

Staphylococcus epidermidis device-related infections: pathogenesis and clinical management

Staphylococcus epidermidis, the most frequently isolated coagulase-negative staphylococcus, is the leading cause of infection related to implanted medical devices (IMDs). This is directly related to its capability to establish multilayered, highly structured biofilms on artificial surfaces. At present, conventional systemic therapies using standard antimicrobial agents represent the main strategy to treat and prevent medical device-associated infections. However, device-related infections are notoriously difficult to treat and bacteria within biofilm communities on the surface of IMDs frequently outlive treatment, and removal of the medical device is often required for successful therapy. Importantly, major advances in this research area have been made, leading to a greater understanding of the complexities of biofilm formation of S. epidermidis and resulting in significant developments in the treatment and prevention of infections related to this member of the coagulase-negative group of staphylococci. This review will examine the pathogenesis of the clinically significant S. epidermidis and provide an overview of the conventional and emerging antibiofilm approaches in the management of medical device-associated infections related to this important nosocomial pathogen.

SoC memory hierarchy derivation from dataflow graphs

Hardware synthesis from dataflow graphs of signal processing systems is a growing research area as focus shifts to high level design methodologies. For data intensive systems, dataflow based synthesis can lead to an inefficient usage of memory due to the restrictive nature of synchronous dataflow and its inability to easily model data reuse. This paper explores how dataflow graph changes can be used to drive both the on-chip and off-chip memory organisation and how these memory architectures can be mapped to a hardware implementation. By exploiting the data reuse inherent to many image processing algorithms and by creating memory hierarchies, off-chip memory bandwidth can be reduced by a factor of a thousand from the original dataflow graph level specification of a motion estimation algorithm, with a minimal increase in memory size. This analysis is verified using results gathered from implementation of the motion estimation algorithm on a Xilinx Virtex-4 FPGA, where the delay between the memories and processing elements drops from 14.2 ns down to 1.878 ns through the refinement of the memory architecture. Care must be taken when modeling these algorithms however, as inefficiencies in these models can be easily translated into overuse of hardware resources.

Citizenship and Structured Dependency: the implications of policy design for senior political power

This paper argues that the structured dependency thesis must be extended to incorporate political power. It outlines a political framework of analysis with which to identify who gains and who loses from social policy. I argue that public policy for older people is a product not only of social structures but also of political decision-making. The Schneider and Ingram (1993) ‘ target populations’ model is used to investigate how the social construction of groups as dependent equates with lower levels of influence on policy making. In United Kingdom and European research, older people are identified as politically quiescent, but conversely in the United States seniors are viewed as one of the most influential and cohesive interest groups in the political culture. Why are American seniors perceived as politically powerful, while older people in Europe are viewed as dependent and politically weak? This paper applies the ‘target populations’ model to senior policy in the Republic of Ireland to investigate how theoretical work in the United States may be used to identify the significance of senior power in policy development. I conclude that research must recognise the connections between power, politics and social constructions to investigate how state policies can influence the likelihood that seniors will resist structured dependency using political means.

Adaptive Fusion Of Particle Filtering And Spatio-Temporal Motion Energy For Human Tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

A review of patents relating to therapeutic angiogenesis using endothelial progenitors and other vasculogenesis-related cell types

Stem and progenitor cells have generated considerable scientific and commercial interest in recent years due to their potential for novel cell therapy for a variety of medical conditions. A highly active research area in the field of regenerative medicine is vascular biology. Blood vessel repair and angiogenesis are key processes with endothelial progenitor cells (EPCs) playing a central role. Clinical trials for ischemic conditions, such as myocardial infarction and peripheral arterial disease, have suggested cell therapies to be feasible, safe, and potentially beneficial. Development of efficient methodologies to deliver EPC-based cytotherapies offers new hope for millions of patients with ischemic conditions. Evidence indicates that EPCs, depending on the subtype, mediate angiogenesis through different mechanisms. Differentiation into endothelium and complete integration into damaged vasculature was the first EPC mechanism to be proposed. However, many studies have demonstrated that vasoregulatory paracrine factor secretion by transplanted cells is also important. Many EPC subsets enhance angiogenesis and promote tissue repair by cytokine release without incorporating into the damaged vasculature. Whatever the mechanism, vascular repair and therapeutic angiogenesis using EPCs represent a realistic treatment option and also provides many commercialization opportunities. This review discusses recent advances in the EPC field whilst recounting relevant patents.

Evaluation of sampling methods for learning from imbalanced data

The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics. © 2013 Springer-Verlag.

On Cherry-Picking ... or Trying to Have One’s Cake and Eating It: past principles and practice in the pursuit of an alternative to [full] EU membership

With the United Kingdom’s continued membership of the EU being increasingly questioned and the Prime Minister, David Cameron, committed to 're-negotiate’ the terms of membership, consideration is being given to what forms alternatives to [full] membership may take. While much current discussion focuses on the advantages and disadvantages of particular existing arrangements (e.g. European Economic Area, Swiss bilateralism), this paper examines the broader principles and practices that have to date underpinned – and undermined – EU’s attempts to develop alternatives to [full] EU membership. Drawing on an analysis of the evolution of association as an alternative to membership, the paper assesses the principled, practical and political limitations the EU faces – and imposes on itself – in offering an acceptable balance of rights and obligations to states not wishing to assume the mantle of full membership. In its assessment the paper considers various proposed models of affiliate and associate membership. It also situates consideration of the UK case in the broader context of the EU’s relations with other European non-member states for which membership may not be achievable and for which alternatives to membership (e.g. a form of privileged partnership) have been proposed. In doing so, the paper reflects on the precedent-setting consequences of any arrangement that the EU might reach with any state re-negotiating membership or withdrawing.