Training aspects for a trawling fishery

Trawling experiments carried out by the United Nations Development Programme Project and the Uganda Department of Fisheries, strongly suggest that the trawling method of fishing, if introduced on Lake Victoria, would bring about a tremendous increase in fish production from the lake. It is recognised, however, that before trawling is introduced, its economic, social, technical, biological and manpower implications must be carefully analysed. I now propose to discuss the training aspects of a trawl fishery on Lake Victoria.

DOPAMINE D-1 RECEPTOR MECHANISMS IN THE COGNITIVE PERFORMANCE OF YOUNG-ADULT AND AGED MONKEYS

Dopamine (DA) D-1 receptor compounds were examined in monkeys for effects on the working memory functions of the prefrontal cortex and on the fine motor abilities of the primary motor cortex. The D-1 antagonist, SCH23390, the partial D-1 agonist, SKF38393, and the full D-1 agonist, dihydrexidine, were characterized in young control monkeys, and in aged monkeys with naturally occurring catecholamine depletion. In addition, SKF38393 was tested in young monkeys experimentally depleted of catecholamines with chronic reserpine treatment. Injections of SCH23390 significantly impaired the memory performance of young control monkeys, but did not impair aged monkeys with presumed catecholamine depletion. Conversely, the partial agonist, SKF38393, improved the depleted monkeys (aged or reserpine-treated) but did not improve young control animals. The full agonist, dihydrexidine, did improve memory performance in young control monkeys, as well as in a subset of aged monkeys. Consistent with D, receptor mechanisms, agonist-induced improvements were blocked by SCH23390. Drug effects on memory performance occurred independently of effects on fine motor performance. These results underscore the importance of DA D-1 mechanisms in cognitive function, and provide functional evidence of DA system degeneration in aged monkeys. Finally, high doses of D-1 agonists impaired memory performance in aged monkeys, suggesting that excessive D-1 stimulation may be deleterious to cognitive function.

Improving effects of huperzine A on spatial working memory in aged monkeys and young adult monkeys with experimental cognitive impairment

Our previous studies demonstrated that huperzine A, a reversible and selective acetylcholinesterase inhibitor, exerts beneficial effects on memory deficits in various rodent models of amnesia. To extend the antiamnesic action of huperzine A to nonhuman primates, huperzine A was evaluated for its ability to reverse the deficits in spatial memory produced by scopolamine in young adult monkeys or those that are naturally occurring in aged monkeys using a delayed-response task. Scopolamine, a muscarinic receptor antagonist, dose dependently impaired performance with the highest dose (0.03 mg/kg, i.m.) producing a significant reduction in choice accuracy in young adult monkeys. The delayed performance changed from an average of 26.8/30 trials correct on saline control to an average of 20.2/30 trials correct after scopolamine administration. Huperzine A (0.01-0.1 mg/kg, i.m.) significantly reversed deficits induced by scopolamine in young adult monkeys on a delayed-response task; performance after an optimal dose (0.1 mg/kg) averaged 25.0/30 correct. In four aged monkeys, huperzine A (0.001-0.01 mg/kg, i.m.) significantly increased choice accuracy from 20.5/30 on saline control to 25.2/30 at the optimal dose (0.001 mg/kg for two monkeys and 0.01 mg/kg for the other two monkeys). The beneficial effects of huperzine A on delayed-response performance were long lasting; monkeys remained improved for about 24 h after a single injection of huperzine A. This study extended the findings that huperzine A improves the mnemonic performance requiring working memory in monkeys, and suggests that huperzine A may be a promising agent for clinical therapy of cognitive impairments in patients with Alzheimer's disease.

Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Factor analysis based VTS discriminative adaptive training

Vector Taylor Series (VTS) model based compensation is a powerful approach for noise robust speech recognition. An important extension to this approach is VTS adaptive training (VAT), which allows canonical models to be estimated on diverse noise-degraded training data. These canonical model can be estimated using EM-based approaches, allowing simple extensions to discriminative VAT (DVAT). However to ensure a diagonal corrupted speech covariance matrix the Jacobian (loading matrix) relating the noise and clean speech is diagonalised. In this work an approach for yielding optimal diagonal loading matrices based on minimising the expected KL-divergence between the diagonal loading matrix and "correct" distributions is proposed. The performance of DVAT using the standard and optimal diagonalisation was evaluated on both in-car collected data and the Aurora4 task. © 2012 IEEE.

N-best error simulation for training spoken dialogue systems

A recent trend in spoken dialogue research is the use of reinforcement learning to train dialogue systems in a simulated environment. Past researchers have shown that the types of errors that are simulated can have a significant effect on simulated dialogue performance. Since modern systems typically receive an N-best list of possible user utterances, it is important to be able to simulate a full N-best list of hypotheses. This paper presents a new method for simulating such errors based on logistic regression, as well as a new method for simulating the structure of N-best lists of semantics and their probabilities, based on the Dirichlet distribution. Off-line evaluations show that the new Dirichlet model results in a much closer match to the receiver operating characteristics (ROC) of the live data. Experiments also show that the logistic model gives confusions that are closer to the type of confusions observed in live situations. The hope is that these new error models will be able to improve the resulting performance of trained dialogue systems. © 2012 IEEE.

Complementary Phone Error training

This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.