North sea oil: Responses to employment opportunities

Britain's new offshore oil industry might have generated much additional employment especially in the building, manning and supply of rigs. But there was a failure at first to take proper advantage; little was done to train people in new skills. Consequently the oil companies brought in equipment and skilled personnel from abroad. Eventually efforts were made to improve matters and the paper examines the steps taken to bring more of the employment benefits to Britain, but finds only partial success.     

Cortical cholinergic impairment and behavioral deficits produced by kainic acid lesions of rat magnocellular basal forebrain.

The rat magnocellular basal forebrain (MNBF) is homologous to the human nucleus basalis of Meynert, a structure implicated in the cholinergic hypothesis of cognitive impairment in Alzheimer's disease (AD). In the present study, 18 male Sprague-Dawley rats with kainic acid lesions in the MNBF were compared with 6 unoperated controls, 10 sham-operated controls, and 6 controls injected with kainic acid in the cortical area directly above the MNBF. MNBF lesions depleted choline acetyltransferase in cortex but not in striatum or hippocampus. Cortical dopamine levels were unchanged; serotonin levels were unchanged in hippocampus and parietal cortex but decreased in frontal cortex. Compared with controls, MNBF-lesioned Ss were impaired in 24-hr retention, but not acquisition, of a passive avoidance task with escapable footshock. The groups did not differ in mean number of daily avoidances on a barpress active avoidance task, although learning was slower in MNBF-lesioned Ss. In a serial spatial discrimination reversal test, MNBF-lesioned Ss performed significantly worse than controls. This model may be useful for studying the role of the cholinergic system in memory and possibly for developing treatment strategies to alleviate the cognitive dysfunction of AD. (63 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Critical Look at Experimental Evaluations of EBL

A number of experimental evaluations ofexplanation-based learning (EBL) have been reported in the literature on machine learning. A close examination of the design of these experiments revelas certain methodological problems that could affect the conclusions drawn from the experiments. This article analyzes some of the more common methodological difficulties, and illustrates them using selected previous studies.     

Self‐Assembled Hybrid Materials Based on Organic Nanocrystals and Carbon Nanotubes

Organic crystalline materials are used as dyes/pigments, pharmaceuticals, and active components of photonic and electronic devices. There is great interest in integrating organic crystals with inorganic and carbon nanomaterials to create nanocomposites with enhanced properties. Such efforts are hampered by the difficulties in interfacing organic crystals with dissimilar materials. Here, an approach that employs organic nanocrystallization is presented to fabricate solution‐processed organic nanocrystal/carbon nanotube (ONC/CNT) hybrid materials based on readily available organic dyes (perylene diimides (PDIs)) and carbon nanotubes. The hybrids are prepared by self‐assembly in aqueous media to afford free‐standing films with tunable CNT content. These exhibit excellent conductivities (as high as 5.78 ± 0.56 S m^?1), and high thermal stability that are superior to common polymer/CNT hybrids. The color of the hybrids can be tuned by adding various PDI derivatives. ONC/CNT hybrids represent a novel class of nanocomposites, applicable as optoelectronic and conductive colorant materials.     

Fast recognition of musical genres using RBF networks

This paper explores the automatic classification of audio tracks into musical genres. Our goal is to achieve human-level accuracy with fast training and classification. This goal is achieved with radial basis function (RBF) networks by using a combination of unsupervised and supervised initialization methods. These initialization methods yield classifiers that are as accurate as RBF networks trained with gradient descent (which is hundreds of times slower). In addition, feature subset selection further reduces training and classification time while preserving classification accuracy. Combined, our methods succeed in creating an RBF network that matches the musical classification accuracy of humans. The general algorithmic contribution of this paper is to show experimentally that RBF networks initialized with a combination of methods can yield good classification performance without relying on gradient descent. The simplicity and computational efficiency of our initialization methods produce classifiers that are fast to train as well as fast to apply to novel data. We also present an improved method for initializing the k-means clustering algorithm, which is useful for both unsupervised and supervised initialization methods.     

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches.     

Beam search algorithms for multilabel learning

Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this paper, we show how to use the classical technique of beam search to solve both these problems. Specifically, we show how to apply beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of datasets show that the proposed improvements yield a state-of-the-art method for multilabel learning.     

Differential privacy based on importance weighting

This paper analyzes a novel method for publishing data while still protecting privacy. The method is based on computing weights that make an existing dataset, for which there are no confidentiality issues, analogous to the dataset that must be kept private. The existing dataset may be genuine but public already, or it may be synthetic. The weights are importance sampling weights, but to protect privacy, they are regularized and have noise added. The weights allow statistical queries to be answered approximately while provably guaranteeing differential privacy. We derive an expression for the asymptotic variance of the approximate answers. Experiments show that the new mechanism performs well even when the privacy budget is small, and when the public and private datasets are drawn from different populations.