A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

Transaction Costs and Organizational Learning in Strategic Oursourcing Relationships

The present paper examines the role of organisational learning and transaction costs economics in strategic outsourcing decisions. Interorganisational learning is critical to competitive success, and organisations often learn more effectively by collaborating with other organisations. However, learning processes may also complicate the process of forming interorganisational partnerships which may increase transaction costs. Based on the literature, the authors develop refutable implications for outsourcing supply chain logistics and a sample of 121 firms in the supply chain logistics industry is used to test the hypotheses. The results show that trust and transaction costs are significant and substantial drivers of strategic outsourcing of supply chain logistics (a strategic flexibility action). Learning intent and knowledge acquisition have no significant influence on the decision to outsource supply chain logistics. The paper concludes with a discussion of the different and often conflicting implications for managing interorganisational learning processes.

A Novel Selective Muscarinic Acetylcholine Receptor Subtype 1 Antagonist Reduces Seizures without Impairing Hippocampus-Dependent Learning

Previous studies suggest that selective antagonists of specific subtypes of muscarinic acetylcholine receptors (mAChRs) may provide a novel approach for the treatment of certain central nervous system (CNS) disorders, including epileptic disorders, Parkinson's disease, and dystonia. Unfortunately, previously reported antagonists are not highly selective for specific mAChR subtypes, making it difficult to definitively establish the functional roles and therapeutic potential for individual subtypes of this receptor subfamily. The M 1 mAChR is of particular interest as a potential target for treatment of CNS disorders. We now report the discovery of a novel selective antagonist of M-1 mAChRs, termed VU0255035 [N-(3-oxo-3-(4-(pyridine-4-yl)piperazin-1-yl)propyl)benzo[c][1,2,5]thiadiazole-4-sulfonamide]. Equilibrium radioligand binding and functional studies demonstrate a greater than 75-fold selectivity of VU0255035 for M-1 mAChRs relative to M-2-M-5. Molecular pharmacology and mutagenesis studies indicate that VU0255035 is a competitive orthosteric antagonist of M-1 mAChRs, a surprising finding given the high level of M-1 mAChR selectivity relative to other orthosteric antagonists. Whole-cell patch-clamp recordings demonstrate that VU0255035 inhibits potentiation of N-methyl-D-aspartate receptor currents by the muscarinic agonist carbachol in hippocampal pyramidal cells. VU0255035 has excellent brain penetration in vivo and is efficacious in reducing pilocarpine-induced seizures in mice. We were surprised to find that doses of VU0255035 that reduce pilo-carpine-induced seizures do not induce deficits in contextual freezing, a measure of hippocampus-dependent learning that is disrupted by nonselective mAChR antagonists. Taken together, these data suggest that selective antagonists of M-1 mAChRs do not induce the severe cognitive deficits seen with nonselective mAChR antagonists and could provide a novel approach for the treatment certain of CNS disorders.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

A leadership framework to support the use of e-learning resources

Aim
This inquiry aims to apply the NHS leadership framework to nurse education for the implementation of e-learning.
Background
Recognition needs to be given to the emerging postgraduate nursing students new status of consumer and the challenge now for nurse education is how to remain relevant and competitive in this consumer led market. The move towards an e-learning paradigm has been suggested as a competitive and contemporary way forward for the student consumer. The successful introduction of e-learning in nurse education will require leadership and a strong organisational management system.
Discussion
Each element of the NHS leadership framework is described and interpreted for application in a higher education setting for the implementation of e-learning.
Conclusions
Change in the delivery of post graduate nurse education is necessary to ensure it remains current and reflective of consumer need in a competitive marketplace. By applying a leadership framework that acknowledges the skills and abilities of staff and encourages the formation of collaborative partnerships from within the wider university community, educators can begin to develop skills and confidence in teaching using e-learning resources.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Can Future Managers and Business Executives be Influenced to Behave more Ethically in the Workplace? The Impact of Approaches to Learning on Business Students’ Cheating Behavior

This study considers the potential for influencing business students to become ethical managers by directing their undergraduate learning environment. In particular, the relationship between business students’ academic cheating, as a predictor of workplace ethical behavior, and their approaches to learning is explored. The three approaches to learning identified from the students’ approaches to learning literature are deep approach, represented by an intrinsic interest in and a desire to understand the subject, surface approach, characterized by rote learning and memorization without understanding, and strategic approach, associated with competitive students whose motivation is the achievement of good grades by adopting either a surface or deep approach. Consistent with the hypothesized theoretical model, structural equation modeling revealed that the surface approach is associated with higher levels of cheating, while the deep approach is related to lower levels. The strategic approach was also associated with less cheating and had a statistically stronger influence than the deep approach. Further, a significantly positive relationship reported between deep and strategic approaches suggests that cheating is reduced when deep and strategic approaches are paired. These findings suggest that future managers and business executives can be influenced to behave more ethically in the workplace by directing their learning approaches. It is hoped that the evidence presented may encourage those involved in the design of business programs to implement educational strategies which optimize students’ approaches to learning towards deep and strategic characteristics, thereby equipping tomorrow’s managers and business executives with skills to recognize and respond appropriately to workplace ethical dilemmas.

Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Traditional heuristic approaches to the Examination Timetabling Problem normally utilize a stochastic method during Optimization for the selection of the next examination to be considered for timetabling within the neighbourhood search process. This paper presents a technique whereby the stochastic method has been augmented with information from a weighted list gathered during the initial adaptive construction phase, with the purpose of intelligently directing examination selection. In addition, a Reinforcement Learning technique has been adapted to identify the most effective portions of the weighted list in terms of facilitating the greatest potential for overall solution improvement. The technique is tested against the 2007 International Timetabling Competition datasets with solutions generated within a time frame specified by the competition organizers. The results generated are better than those of the competition winner in seven of the twelve examinations, while being competitive for the remaining five examinations. This paper also shows experimentally how using reinforcement learning has improved upon our previous technique.