999 resultados para Invariant Bi-separable preference
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The time of day during which P. monodon feeds at different depth levels in earthen ponds, and its preference for three types of tilapia feeds (dry, fresh and fermented) were determined. It was observed that P. monodon concentrated at the bottom beds during the day and along the periphery of dikes during night-time, with a slight tendency to swim and feed towards the surface as darkness increased. P. monodon showed special preference for dried tilapia compared to fresh and fermented tilapia. P. monodon also showed adaptability to the platform method of feeding, especially during night-time.
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Salinity preference of the milkfish fry is described. Milkfish fry which were acclimatized to different salinities have preference for certain salinity which is 32 ppt. Majority of the fry in the control tanks usually prefer to stay either at the bottom or at the uppermost layer; only a few stay at the middle layer. This strongly suggests that milkfish fry that are not caught along the shore by fishermen usually tend to migrate to waters of high salinity. Furthermore, salinity preference of milkfish fry does not change with age.
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Status papers on resources, livelihoods and ecosystem approach to fisheries in the Gulf of Mannar(GoM) were presented.Five priority initiatives were agreed: collaborative effort in conservation and management of charismatic species (e.g. dugong),capacity building and training,education and awareness building, strengthening of data collection and sharing of information.
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The study of food preference is necessitated by the need to promote coastal culture of shrimps in Pakistan. The cultured Penaeus monodon was selected for study. Food preferences have been examined through the analysis of the gut contents. The shrimp shows a seasonal variation in its preference to food and feeding.
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It is well known that the cholinergic system plays a crucial role in learning and memory. Psychopharmacological studies in humans and animals have shown that a systemic cholinergic blockade may induce deficits in learning and memory. Accumulated studies h
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Learning and memory play an important role in morphine addiction. Status epilepticus (SE) can impair the spatial and emotional learning and memory. However, little is known about the effects of SE on morphine-induced conditioned place preference (CPP). Th
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Drug addiction is increasingly viewed as the expression of abnormal associative learning following repeated exposures to the drugs of abuse Previous I studies have demonstrated that the patterns of repetition such as frequency and spacing are important to many kinds of learning and memory retention We hypothesized that drug repetition pattern might affect the reward-related learning although the total doses of the drug were the same. In the present study, we tested morphine-induced place preference following either regular or irregular pattern of morphine pairing in rats Regular morphine group received morphine administration daily at a regular time with the same dose Irregular morphine groups received morphine administration either at the same time but irregular doses, irregular time but same dose, or irregular time and irregular doses. We found that rats, who received irregular morphine pairing, exhibited similar acquisition of peace preference but different preference retentions compared with regular morphine-treated rats after the same total dose of morphine Rats, who received morphine administration at the same time but irregular doses and at irregular time and irregular doses, showed rapid disruption of place preference than the regular morphine group. Rats, who received morphine at irregular time but the same dose, showed similar retention of place preference to regular morphine group Our results suggest that the pattern of drug pairing plays an important role in the retention of reward-related memory This study may provide new evidence to broaden our understanding of the development and maintenance of drug craving (C) 2009 Elsevier B V. All rights reserved
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Learned association between drugs of abuse and context is essential for the formation of drug conditioned place preference (CPP), which is believed to engage many brain regions including hippocampus, and nucleus accumbens (NAc). The underlying mechanisms
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Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.
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This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2011 IEEE.
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In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.