207,326 research outputs found
A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots
Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantificatio
Domain Decomposition for Stochastic Optimal Control
This work proposes a method for solving linear stochastic optimal control
(SOC) problems using sum of squares and semidefinite programming. Previous work
had used polynomial optimization to approximate the value function, requiring a
high polynomial degree to capture local phenomena. To improve the scalability
of the method to problems of interest, a domain decomposition scheme is
presented. By using local approximations, lower degree polynomials become
sufficient, and both local and global properties of the value function are
captured. The domain of the problem is split into a non-overlapping partition,
with added constraints ensuring continuity. The Alternating Direction
Method of Multipliers (ADMM) is used to optimize over each domain in parallel
and ensure convergence on the boundaries of the partitions. This results in
improved conditioning of the problem and allows for much larger and more
complex problems to be addressed with improved performance.Comment: 8 pages. Accepted to CDC 201
On Learning Equilibria (Revised June 2003)
We investigate an inflationary overlapping generations model where households predict future inflation rates by running a least squares regression of inflation rates or prices on their past levels. We critically examine the results on learning equilibria obtained by Bullard (1994) and Schönhofer (1999) in this framework. They show that an increase in the money growth rate may lead to limit cycles and endogenous business cycles. We suggest an alternative estimation procedure, that starts from the same perceived law of motion, but is more sensible from an econometrician's point of view. We prove that for this estimation procedure there is global convergence on the monetary steady for a large set of savings functions. We also study, in a heterogeneou agents framework, evolutionary competition between the two estimation procedures, where the fraction of the population using a certain estimation procedure is determined by its past average quadratic forecast error. Interestingly, the more sensible estimation procedure is not always able to drive out the other estimation procedure, and endogenous business cycles may still be observed in this heterogeneous world.
Comparative investigation of two different self-organizing map-based wavelength selection approaches for analysis of binary mixtures with strongly overlapping spectral lines
Purpose: To demonstrate the ability and investigate the performance of two different wavelength selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) regression for analysis of pharmaceutical binary mixtures with strongly overlapping spectra.Methods: Two different variable selection methods were compared, namely, SOM1-PLS and SOM2- PLS. The main difference between these methods involved the structure of neurons in input layer and the algorithm for variable selection. Adjustable parameters for each technique were optimized for better comparison. The performance of these methods was statistically verified for predictive ability using both synthetic mixtures and a real combination product of sulfamethoxazole (SMX) and trimethoprim (TMP), which exhibited strongly overlapping of spectral lines.Results: The results obtained indicate that SOM2-PLS was more efficient than SOM1-PLS technique with 30 and 6 % improvement in predictive ability for SMX and TMP, respectively. Furthermore, the mean difference between the results obtained from SOM2-PLS method and those from the official method was not statistically significant as p-value was more than 0.01.Conclusion: Although, SOM2-PLS method is more efficient than SOM1-PLS method for the analysis of pharmaceutical binary mixtures with severely overlapping spectra, some problems associated with SOM2-PLS technique include difficult computations of some parameters.Keywords: Co-trimoxazole, Self-organizing map, Wavelength selection, Pharmaceutical analysis, Overlapping spectral line
Do Voluntary Commons Associations Deliver Sustainable Grazing Outcomes? An Empirical Study of England
In 1965, the Commons Registration Act came into force in England and Wales. The Act led to the removal of the capacity of commoners to regulate the intensity of grazing via traditional legal means. From this policy shock a number of voluntary commons associations were formed. These voluntary groups relied on their members to agree upon how the commons should be managed. Using two-stage least squares regression analysis we find that commons governed by these associations are much more likely to produce sustainable grazing outcomes. These results are robust to the existence of a variety of controls, including overlapping institutional frameworks. Importantly, they highlight the ability of voluntary environmental organisations to deliver sustainable environmental outcomes
Automated tracking of colloidal clusters with sub-pixel accuracy and precision
Quantitative tracking of features from video images is a basic technique
employed in many areas of science. Here, we present a method for the tracking
of features that partially overlap, in order to be able to track so-called
colloidal molecules. Our approach implements two improvements into existing
particle tracking algorithms. Firstly, we use the history of previously
identified feature locations to successfully find their positions in
consecutive frames. Secondly, we present a framework for non-linear
least-squares fitting to summed radial model functions and analyze the accuracy
(bias) and precision (random error) of the method on artificial data. We find
that our tracking algorithm correctly identifies overlapping features with an
accuracy below 0.2% of the feature radius and a precision of 0.1 to 0.01 pixels
for a typical image of a colloidal cluster. Finally, we use our method to
extract the three-dimensional diffusion tensor from the Brownian motion of
colloidal dimers.Comment: 20 pages, 8 figures. Non-revised preprint version, please refer to
http://dx.doi.org/10.1088/1361-648X/29/4/04400
360-degree Video Stitching for Dual-fisheye Lens Cameras Based On Rigid Moving Least Squares
Dual-fisheye lens cameras are becoming popular for 360-degree video capture,
especially for User-generated content (UGC), since they are affordable and
portable. Images generated by the dual-fisheye cameras have limited overlap and
hence require non-conventional stitching techniques to produce high-quality
360x180-degree panoramas. This paper introduces a novel method to align these
images using interpolation grids based on rigid moving least squares.
Furthermore, jitter is the critical issue arising when one applies the
image-based stitching algorithms to video. It stems from the unconstrained
movement of stitching boundary from one frame to another. Therefore, we also
propose a new algorithm to maintain the temporal coherence of stitching
boundary to provide jitter-free 360-degree videos. Results show that the method
proposed in this paper can produce higher quality stitched images and videos
than prior work.Comment: Preprint versio
Unsupervised learning of overlapping image components using divisive input modulation
This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance
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