Pregabalin - learning from gabapentin?

The author overviews the use of pregabalin in neuropathic pain. (non-author abstract)

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Learning from the story of a great leader

This paper reports on research findings from a larger study which seeks to understand leadership from the experiences of well-known and well-recognised Australian leaders across a spectrum of endeavours such as the arts, business, science, the law and politics. To date there appears to be limited empirical research that has investigated the insights of Australian leaders regarding their leadership experiences, beliefs and practices. In this paper, the leadership story of a well-respected medical scientist is discussed revealing the contextual factors that influenced her thinking about leadership as well as the key values she embodies as a leader. The paper commences by briefly considering some of the salient leadership literature in the field. In particular, two prominent theoretical frameworks provided by Leavy (2003)and Kouzes and Posner (2002) are explored. While Leavy’s framework construes leadership as consisting of three “C’s” – context , conviction and credibility, Kouzes and Posner (2002)refer to five practices of exemplary leadership. The paper provides a snapshot of the life forces and context that played an important role in shaping the leader’s views and practices. An analytical discussion of these practices is considered in the light of the earlier frameworks identified. Some implications of the findings from this non-education context for those in schools are briefly noted.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

Climate and causation in the Swedish Iron Age : Learning from the present to understand the past

The paper reassesses the role of climate as a factor shaping changes in settlement and landscape in the Swedish Iron Age (500 BC to AD 1050). Two reasons motivate this re-evaluation. First, high-resolution data based on climate proxies from the natural sciences are now increasingly available. Second, the climate-related social sciences have yielded conceptual and theoretical developments regarding vulnerability and adaptability in the present and recent past, creating new ways to analyse the effects of climatic versus societal factors on societies in the more distant past. Recent research in this field is evaluated and the explicitly climate deterministic standpoint of many recent natural science texts is criticized. Learning from recent approaches to climate change in the social sciences is crucial for understanding society–climate relationships in the past. The paper concludes that we are not yet in a position to fully evaluate the role of the new evidence of abrupt climate change in 850 BC, at the beginning of the Iron Age. Regarding the crisis in the mid first millennium AD, however, new climate data indicate that a dust veil in AD 536–537 might have aggravated the economic and societal crisis known from previous research.

Ontologies, taxonomies, thesauri:learning from texts

The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus.

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.

Statistical guidelines for the analysis of data obtained from one or both eyes

Purpose - Measurements obtained from the right and left eye of a subject are often correlated whereas many statistical tests assume observations in a sample are independent. Hence, data collected from both eyes cannot be combined without taking this correlation into account. Current practice is reviewed with reference to articles published in three optometry journals, viz., Ophthalmic and Physiological Optics (OPO), Optometry and Vision Science (OVS), Clinical and Experimental Optometry (CEO) during the period 2009–2012. Recent findings - Of the 230 articles reviewed, 148/230 (64%) obtained data from one eye and 82/230 (36%) from both eyes. Of the 148 one-eye articles, the right eye, left eye, a randomly selected eye, the better eye, the worse or diseased eye, or the dominant eye were all used as selection criteria. Of the 82 two-eye articles, the analysis utilized data from: (1) one eye only rejecting data from the adjacent eye, (2) both eyes separately, (3) both eyes taking into account the correlation between eyes, or (4) both eyes using one eye as a treated or diseased eye, the other acting as a control. In a proportion of studies, data were combined from both eyes without correction. Summary - It is suggested that: (1) investigators should consider whether it is advantageous to collect data from both eyes, (2) if one eye is studied and both are eligible, then it should be chosen at random, and (3) two-eye data can be analysed incorporating eyes as a ‘within subjects’ factor.

Learning from openness : the dynamics of breadth in external innovation linkages

We explore how openness in terms of external linkages generates learning effects, which enable firms to generate more innovation outputs from any given breadth of external linkages. Openness to external knowledge sources, whether through search activity or linkages to external partners in new product development, involves a process of interaction and information processing. Such activities are likely to be subject to a learning process, as firms learn which knowledge sources and collaborative linkages are most useful to their particular needs, and which partnerships are most effective in delivering innovation performance. Using panel data from Irish manufacturing plants, we find evidence of such learning effects: establishments with substantial experience of external collaborations in previous periods derive more innovation output from openness in the current period. © 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.

Designing post-disaster supply chains:learning from housing reconstruction projects

Post-disaster housing reconstruction projects face several challenges. Resources and material supplies are often scarce; several and different types of organizations are involved, while projects must be completed as quickly as possible to foster recovery. Within this context, the chapter aims to increase the understanding of relief supply chain design in reconstruction. In addition, the chapter is introducing a community based and beneficiary perspective to relief supply chains by evaluating the implications of local components for supply chain design in reconstruction. This is achieved through the means of secondary data analysis based on the evaluation reports of two major housing reconstruction projects that took place in Europe the last decade. A comparative analysis of the organizational designs of these projects highlights the ways in which users can be involved. The performance of reconstruction supply chains seems to depend to a large extent on the way beneficiaries are integrated in supply chain design impacting positively on the effectiveness of reconstruction supply chains.

Reconstruction of an acoustically sound-soft obstacle from one incident field and the far field pattern

The problem considered is that of determining the shape of a plane acoustically sound-soft obstacle from the knowledge of the far-field pattern for one time-harmonic incident field. An iterative procedure is proposed based on two boundary integrals representing the incident field and the far-field pattern, respectively. Numerical examples are included which show that the procedure gives accurate numerical approximations in relatively few iterations.

Learning from mother nature:exploiting a biological antioxidant for the melt stabilisation of polymers

An overview of the antioxidant role of the biologically active form of vitamin E, α-tocopherol, in polyolefins is discussed. The effect of the vitamin antioxidant on the melt and colour stability of polyethylene (PE) and polypropylene (PP) is highlighted. It is shown that tocopherol is a highly effective antioxidant that results in superior melt stabilisation of polyolefins particularly when used at much lower concentration than that needed for conventional synthetic hindered phenol processing stabilisers. As with other hindered phenols,α-tocopherol imparts also some colour to the polymer but this is shown to be reduced drastically in the presence of other antioxidants, such as phosphites, or other additives, such as polyhydric alcohols.