204 research outputs found
Cover Tree Bayesian Reinforcement Learning
This paper proposes an online tree-based Bayesian approach for reinforcement
learning. For inference, we employ a generalised context tree model. This
defines a distribution on multivariate Gaussian piecewise-linear models, which
can be updated in closed form. The tree structure itself is constructed using
the cover tree method, which remains efficient in high dimensional spaces. We
combine the model with Thompson sampling and approximate dynamic programming to
obtain effective exploration policies in unknown environments. The flexibility
and computational simplicity of the model render it suitable for many
reinforcement learning problems in continuous state spaces. We demonstrate this
in an experimental comparison with least squares policy iteration
A Bayesian Ensemble Regression Framework on the Angry Birds Game
An ensemble inference mechanism is proposed on the Angry Birds domain. It is
based on an efficient tree structure for encoding and representing game
screenshots, where it exploits its enhanced modeling capability. This has the
advantage to establish an informative feature space and modify the task of game
playing to a regression analysis problem. To this direction, we assume that
each type of object material and bird pair has its own Bayesian linear
regression model. In this way, a multi-model regression framework is designed
that simultaneously calculates the conditional expectations of several objects
and makes a target decision through an ensemble of regression models. Learning
procedure is performed according to an online estimation strategy for the model
parameters. We provide comparative experimental results on several game levels
that empirically illustrate the efficiency of the proposed methodology.Comment: Angry Birds AI Symposium, ECAI 201
Microarray sub-grid detection: A novel algorithm
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis LtdA novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing
A novel neural network approach to cDNA microarray image segmentation
This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(Âź) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Data-driven estimation of flightsâ hidden parameters
This paper presents a data-driven methodology for the estimation of flightsâ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs.This work has received funding from SESAR Joint Undertaking
(JU) within SIMBAD project under grant agreement No
894241. The JU receives support from the European Unionâs
Horizon 2020 research and innovation programme and the
SESAR JU members other than the UnionPeer ReviewedPostprint (author's final draft
Effect of solvent and extraction temperatures on the antioxidant potential of traditional stoned table olives âalcaparrasâ
This paper reports the first approach to the antioxidant potential evaluation of traditional stoned table olives ââalcaparrasââ. This kind
of olives are largely produced and consumed in TrĂĄs-os-Montes region (Northeast of Portugal). Different solvents and temperature
extraction conditions were employed in order to achieve the best method to obtain phenolic compounds and a higher antioxidant
activity. The optimum method (water at boiling temperature) was applied on 10 samples from the traditional market. The total phenol
content ranged between 5.58mg gallic acid equivalents (GAE)/g and 29.88mg GAE/g and effective concentration (EC50) values were in
the range 0.36â1.64 and 0.34â1.72 mg/mL for reducing power and radical scavenging effect, respectively. A significantly negative linear
regression was observed between the total phenol content found in the samples and its antioxidant activity
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