944 resultados para Almost Optimal Density Function


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Poly(ε-caprolactone) (PCL) fibers produced by wet spinning from solutions in acetone under low-shear (gravity-flow) conditions resulted in fiber strength of 8 MPa and stiffness of 0.08 Gpa. Cold drawing to an extension of 500% resulted in an increase in fiber strength to 43 MPa and stiffness to 0.3 GPa. The growth rate of human umbilical vein endothelial cells (HUVECs) (seeded at a density of 5 × 104 cells/mL) on as-spun fibers was consistently lower than that measured on tissue culture plastic (TCP) beyond day 2. Cell proliferation was similar on gelatin-coated fibers and TCP over 7 days and higher by a factor of 1.9 on 500% cold-drawn PCL fibers relative to TCP up to 4 days. Cell growth on PCL fibers exceeded that on Dacron monofilament by at least a factor of 3.7 at 9 days. Scanning electron microscopy revealed formation of a cell layer on samples of cold-drawn and gelatin-coated fibers after 24 hours in culture. Similar levels of ICAM-1 expression by HUVECs attached to PCL fibers and TCP were measured using RT-PCR and flow cytometry, indicative of low levels of immune activation. Retention of a specific function of HUVECs attached to PCL fibers was demonstrated by measuring their immune response to lipopolysaccharide. Levels of ICAM-1 expression increased by approximately 11% in cells attached to PCL fibers and TCP. The high fiber compliance, favorable endothelial cell proliferation rates, and retention of an important immune response of attached HUVECS support the use of gravity spun PCL fibers for three-dimensional scaffold production in vascular tissue engineering. © Mary Ann Liebert, Inc.

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Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

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On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.

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Training Mixture Density Network (MDN) configurations within the NETLAB framework takes time due to the nature of the computation of the error function and the gradient of the error function. By optimising the computation of these functions, so that gradient information is computed in parameter space, training time is decreased by at least a factor of sixty for the example given. Decreased training time increases the spectrum of problems to which MDNs can be practically applied making the MDN framework an attractive method to the applied problem solver.

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We review recent theoretical progress on the statistical mechanics of error correcting codes, focusing on low-density parity-check (LDPC) codes in general, and on Gallager and MacKay-Neal codes in particular. By exploiting the relation between LDPC codes and Ising spin systems with multispin interactions, one can carry out a statistical mechanics based analysis that determines the practical and theoretical limitations of various code constructions, corresponding to dynamical and thermodynamical transitions, respectively, as well as the behaviour of error-exponents averaged over the corresponding code ensemble as a function of channel noise. We also contrast the results obtained using methods of statistical mechanics with those derived in the information theory literature, and show how these methods can be generalized to include other channel types and related communication problems.

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We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

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The security and reliability of a class of public-key cryptosystems against attacks by unauthorized parties, who had acquired partial knowledge of one or more of the private key components and/or of the message, were discussed. The standard statistical mechanical methods of dealing with diluted spin systems with replica symmetric considerations were analyzed. The dynamical transition which defined decryption success in practical situation was studied. The phase diagrams which showed the dynamical threshold as a function of the partial acquired knowledge of the private key were also presented.

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In industrialised countries age-related macular disease (ARMD) is the leading cause of visual loss in older people. Because oxidative stress is purported to be associated with an increased risk of disease development the role of antioxidant supplementation is of interest. Lutein is a carotenoid antioxidant that accumulates within the retina and is thought to filter blue light. Increased levels of lutein have been associated with reduced risk of developing ARMD and improvements in visual and retinal function in eyes with ARMD. The aim of this randomised controlled trial (RCT) was to investigate the effect of a lutein-based nutritional supplement on subjective and objective measures of visual function in healthy eyes and in eyes with age-related maculopathy (ARM) – an early form of ARMD. Supplement withdrawal effects were also investigated. A sample size of 66 healthy older (HO), healthy younger (HY), and ARM eyes were randomly allocated to receive a lutein-based supplement or no treatment for 40 weeks. The supplemented group then stopped supplementation to look at the effects of withdrawal over a further 20 weeks. The primary outcome measure was multifocal electroretinogram (mfERG) N1P1 amplitude. Secondary outcome measures were mfERG N1, P1 and N2 latency, contrast sensitivity (CS), Visual acuity (VA) and macular pigment optical density (MPOD). Sample sizes were sufficient for the RCT to have an 80% power to detect a significant clinical effect at the 5% significance level for all outcome measures when the healthy eye groups were combined, and CS, VA and mfERG in the ARM group. This RCT demonstrates significant improvements in MPOD in HY and HO supplemented eyes. When HY and HO supplemented groups were combined, MPOD improvements were maintained, and mfERG ring 2 P1 latency became shorter. On withdrawal of the supplement mfERG ring 1 N1P1 amplitude reduced in HO eyes. When HO and HY groups were combined, mfERG ring 1 and ring 2 N1P1 amplitudes were reduced. In ARM eyes, ring 3 N2 latency and ring 4 P1 latency became longer. These statistically significant changes may not be clinically significant. The finding that a lutein-based supplement increases MPOD in healthy eyes, but does not increase mfERG amplitudes contrasts with the CARMIS study and contributes to the debate on the use of nutritional supplementation in ARM.

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Receptor activity modifying protein 1 (RAMP1) is an integral component of several receptors including the calcitonin gene-related peptide (CGRP) receptor. It forms a complex with the calcitonin receptor-like receptor (CLR) and is required for receptor trafficking and ligand binding. The N-terminus of RAMP1 comprises three helices. The current study investigated regions of RAMP1 important for CGRP or CLR interactions by alanine mutagenesis. Modeling suggested the second and third helices were important in protein-protein interactions. Most of the conserved residues in the N-terminus (M48, W56, Y66, P85, N66, H97, F101, D113, P114, P115), together with a further 13 residues spread throughout three helices of RAMP1, were mutated to alanine and coexpressed with CLR in Cos 7 cells. None of the mutations significantly reduced RAMP expression. Of the nine mutants from helix 1, only M48A had any effect, producing a modest reduction in trafficking of CLR to the cell surface. In helix 2 Y66A almost completely abolished CLR trafficking; L69A and T73A reduced the potency of CGRP to produce cAMP. In helix 3, H97A abolished CLR trafficking; P85A, N86A, and F101A had caused modest reductions in CLR trafficking and also reduced the potency of CGRP on cAMP production. F93A caused a modest reduction in CLR trafficking alone and L94A increased cAMP production. The data are consistent with a CLR recognition site particularly involving Y66 and H97, with lesser roles for adjacent residues in helix 3. L69 and T73 may contribute to a CGRP recognition site in helix 2 also involving nearby residues.

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We investigate the feasibility of simultaneous suppressing of the amplification noise and nonlinearity, representing the most fundamental limiting factors in modern optical communication. To accomplish this task we developed a general design optimisation technique, based on concepts of noise and nonlinearity management. We demonstrate the immense efficiency of the novel approach by applying it to a design optimisation of transmission lines with periodic dispersion compensation using Raman and hybrid Raman-EDFA amplification. Moreover, we showed, using nonlinearity management considerations, that the optimal performance in high bit-rate dispersion managed fibre systems with hybrid amplification is achieved for a certain amplifier spacing – which is different from commonly known optimal noise performance corresponding to fully distributed amplification. Required for an accurate estimation of the bit error rate, the complete knowledge of signal statistics is crucial for modern transmission links with strong inherent nonlinearity. Therefore, we implemented the advanced multicanonical Monte Carlo (MMC) method, acknowledged for its efficiency in estimating distribution tails. We have accurately computed acknowledged for its efficiency in estimating distribution tails. We have accurately computed marginal probability density functions for soliton parameters, by numerical modelling of Fokker-Plank equation applying the MMC simulation technique. Moreover, applying a powerful MMC method we have studied the BER penalty caused by deviations from the optimal decision level in systems employing in-line 2R optical regeneration. We have demonstrated that in such systems the analytical linear approximation that makes a better fit in the central part of the regenerator nonlinear transfer function produces more accurate approximation of the BER and BER penalty. We present a statistical analysis of RZ-DPSK optical signal at direct detection receiver with Mach-Zehnder interferometer demodulation

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In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise, in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and; hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

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We report on a new technique to reconstruct the 3D dielectric function change in transparent dielectric materials and the application of the technique for on-line monitoring of refractive index modification in BK7 glass during direct femtosecond laser microfabrication. The complex optical field scattered from the modified region is measured using two-beam, single-shot interferogram and the distribution of the modified refractive index is reconstructed by numerically solving the inverse scattering problem in Born approximation. The optical configuration suggested is further development of digital holographic microscopy (DHM). It takes advantage of high spatial resolution and almost the same optical paths for both interfering beams, and allows ultrafast time resolution.

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Computer-Based Learning systems of one sort or another have been in existence for almost 20 years, but they have yet to achieve real credibility within Commerce, Industry or Education. A variety of reasons could be postulated for this, typically: - cost - complexity - inefficiency - inflexibility - tedium Obviously different systems deserve different levels and types of criticism, but it still remains true that Computer-Based Learning (CBL) is falling significantly short of its potential. Experience of a small, but highly successful CBL system within a large, geographically distributed industry (the National Coal Board) prompted an investigation into currently available packages, the original intention being to purchase the most suitable software and run it on existing computer hardware, alongside existing software systems. It became apparent that none of the available CBL packages were suitable, and a decision was taken to develop an in-house Computer-Assisted Instruction system according to the following criteria: - cheap to run; - easy to author course material; - easy to use; - requires no computing knowledge to use (as either an author or student) ; - efficient in the use of computer resources; - has a comprehensive range of facilities at all levels. This thesis describes the initial investigation, resultant observations and the design, development and implementation of the SCHOOL system. One of the principal characteristics c£ SCHOOL is that it uses a hierarchical database structure for the storage of course material - thereby providing inherently a great deal of the power, flexibility and efficiency originally required. Trials using the SCHOOL system on IBM 303X series equipment are also detailed, along with proposed and current development work on what is essentially an operational CBL system within a large-scale Industrial environment.

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Endogenous glucocorticoids and serotonin have been implicated in the pathophysiology of depression, anxiety and schizophrenia. This thesis investigates the potential of downregulating expression of central Type II glucocorticoid receptors (GR) both in vitro and in vivo, with empirically-designed antisense oligodeoxynucleotides (ODN), to characterise GR modulation of 5-HT2A receptor expression using quantitative RT-PCR, Western blot analysis and radioligand binding. The functional consequence of GR downregulation is also determined by measuring 1-(2,5-dimethoxy 4-iodophenyl)-2-amino propane hydrochloride (DOI) mediated 5-HT2A receptor specific headshakes. Using a library of random antisense ODN probes, RNAse H accessibility mapping of T7-primed, in vitro transcribed GR mRNA revealed several potential cleavage sites and identified an optimally effect GR antisense ODN sequence of 21-mer length (GRAS5). In vitro efficacy studies using rat C6 glioma cells showed a 56% downregulation in GR mRNA levels and 80% downregulation in GR protein levels. In the same cells a 29% upregulation in 5-HT2A mRNA levels and 32% upregulation in 5-HT2A protein levels was revealed. This confirmed the optimal nature of the GRAS5 sequence to produce marked inhibition of GR gene expression, and also revealed GR modulation of the 50-HT2A receptor subtype in C6 glioma cells to be a tonic repression of receptor expression. The distribution of a fluorescently-labelled GRAS5 ODN was detected in diverse areas of the rat brain after single ICV administration, although this fluorescence signal was not sustained over a period of 5 days. However, fluorescently-labelled GRAS5 ODN, when formulated in polymer microspheres, showed diverse distribution in the brain which was maintained for 5 days following a single ICV administration. This produced no apparent neurotoxic effects on rat behaviour and hypothalamic-pituitary-adrenal (HPA) axis homeostasis. Furthermore, a single polymer microsphere injection ICV proved to be an effective means of delivering antisense ODNs and this was adopted for the in vivo efficacy studies. In vivo characterisation of GRAS5 revealed marked downregulation of GR mRNA in rat brain regions such as the frontal cortex (26%), hippocampus (35%), and hypothalamus (39%). Downregulation of GR protein was also revealed in frontal cortex (67%), hippocampus (76%), and hypothalamus (80%). In the same animals upregulation of 5-HT2A mRNA levels was shown in frontal cortex (13%), hippocampus (7%), and hypothalamus (5%) while upregulation in 5-HT2A protein levels was shown in frontal cortex (21 %). This upregulation in 5-HT2A receptor density as a result of antisense-mediated inhibition of GR was further confirmed by a 55% increase in DOl-mediated 5-HT2A receptor specific headshakes. These results demonstrate that GR is involved in tonic inhibitory regulation of 5-HT2A receptor expression and function in vivo, thus providing the potential to control 5-HT2A-linked disorders through corticosteroid manipulation. These experiments have therefore established an antisense approach which can be used to investigate pharmacological characteristics of receptors.

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This thesis first considers the calibration and signal processing requirements of a neuromagnetometer for the measurement of human visual function. Gradiometer calibration using straight wire grids is examined and optimal grid configurations determined, given realistic constructional tolerances. Simulations show that for gradiometer balance of 1:104 and wire spacing error of 0.25mm the achievable calibration accuracy of gain is 0.3%, of position is 0.3mm and of orientation is 0.6°. Practical results with a 19-channel 2nd-order gradiometer based system exceed this performance. The real-time application of adaptive reference noise cancellation filtering to running-average evoked response data is examined. In the steady state, the filter can be assumed to be driven by a non-stationary step input arising at epoch boundaries. Based on empirical measures of this driving step an optimal progression for the filter time constant is proposed which improves upon fixed time constant filter performance. The incorporation of the time-derivatives of the reference channels was found to improve the performance of the adaptive filtering algorithm by 15-20% for unaveraged data, falling to 5% with averaging. The thesis concludes with a neuromagnetic investigation of evoked cortical responses to chromatic and luminance grating stimuli. The global magnetic field power of evoked responses to the onset of sinusoidal gratings was shown to have distinct chromatic and luminance sensitive components. Analysis of the results, using a single equivalent current dipole model, shows that these components arise from activity within two distinct cortical locations. Co-registration of the resulting current source localisations with MRI shows a chromatically responsive area lying along the midline within the calcarine fissure, possibly extending onto the lingual and cuneal gyri. It is postulated that this area is the human homologue of the primate cortical area V4.