A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.     

Image staining and differential diagnosis of ultrasound scans based on the Mahalanobis distance

The covariance matrices associated with each state of health or disease from a previous study are used as the basis of an image staining display technique for aid in quantitative differential diagnosis. A state of health or disease is chosen by the clinician: this selects the covariance matrix from the data base. A region of interest (ROI) is then scrolled through an abdominal B-scan. For each position of the ROI a point in the four-dimensional feature space is calculated. A natural measure of the distance of this point from the center of mass (multivariate mean) of the disease class is calculated in terms of the covariance matrix of this class; this measure is the Mahalanobis distance. The confidence level for acceptance or rejection of the hypothesized disease class is obtained from the probability distribution of this distance, the T(2) probability law. This confidence level is color coded and used as a color stain that overlays the original scan at that position. The variability of the calculated features is studied as a function of ROI size, or the spatial resolution of the color coded image, and it is found that for an ROI in the neighborhood of 4 cm(2) most of the variability due to the finite number of independent samples (speckles) is averaged out, leaving the "noise floor" associated with inter- and intra-patient variability. ROIs on the order of 1 cm(2) may result with technical advances in B-scan resolution. A small number of points on organ boundaries are entered by the user, to fit with arcs of ellipses to be used to switch between organ (liver and kidney) data bases as the ROI encounters the boundary. By selecting in turn various state-of-health or state-of-disease databases, such images of confidence levels may be used for quantitative differential diagnosis. The method is not limited to ultrasound, being applicable in principle to features obtained from any modality or multimodality combination.     

Autoimmunity, immunodeficiency and mucosal infections: chronic intestinal inflammation as a sensitive indicator of immunoregulatory defects in response to normal luminal microflora

Despite the fact that target antigens and the genetic basis of several autoimmune diseases are now better understood, the initial events leading to a loss of tolerance towards self-components remain unknown. One of the most attractive explanations for autoimmune phenomena involves various infections as possible natural events capable of initiating the process in genetically predisposed individuals. The most accepted explanation of how infection causes autoimmunity is based on the concept of "molecular mimicry" (similarity between the epitopes of an autoantigen and the epitopes in the environmental antigen). Infectious stimuli may also participate in the development of autoimmunity by inducing an increased expression of stress proteins (hsp), chaperones and transplantation antigens, which leads to abnormal processing and presentation of self antigens. Superantigens are considered to be one of the most effective bacterial components to induce inflammatory reactions and to take part in the development and course of autoimmune mechanisms. It has long been known that defects in the host defense mechanism render the individual susceptible to infections caused by certain microorganisms. Impaired exclusion of microbial antigens can lead to chronic immunological activation which can affect the tolerance to self components. Defects in certain components of the immune system are associated with a higher risk of a development of autoimmune disease. The use of animal models for the studies of human diseases with immunological pathogenesis has provided new insights into the influence of immunoregulatory factors and the lymphocyte subsets involved in the development of disease. One of the most striking conclusion arising from work with genetically engineered immunodeficient mouse models is the existence of a high level of redundancy of the components of the immune system. However, when genes encoding molecules involved in T cell immunoregulatory functions are deleted, spontaneous chronic inflammation of the gut mucosa (similar to human inflammatory bowel disease) develops. Surprisingly, when such immunocompromised animals were placed into germfree environment, intestinal inflammation did not develop. Impairment of the mucosal immune response to the normal bacterial flora has been proposed to play a crucial role in the pathogenesis of chronic intestinal inflammation. The use of immunodeficient models colonized with defined microflora for the analysis of immune reactivity will shed light on the mode of action of different immunologically important molecules responsible for the delicate balance between luminal commensals, nonspecific and specific components of the mucosal immune system.     

Modelling the three-dimensional elastic constants of parallel-fibred and lamellar bone

The complex hierarchical structure of lamellar bone makes understanding structure–mechanical function relations, very difficult. We approach the problem by first using the relatively simple structure of parallel-fibred bone to construct a mathematical model for calculating Young's moduli in three-dimensions. Parallel-fibred bone is composed essentially of arrays of mineralized collagen fibrils, which are also the basic structural motif of the individual lamellae of lamellar bone. Parallel-fibred bone structure has orthotropic symmetry. As the sizes and shapes of crystals in bone are not well known, the model is also used to compare the cases of platelet-, ribbon- and sheet-reinforced composites. The far more complicated rotated plywood structure of lamellar bone results in the loss of the orthotropic symmetry of individual lamellae. The mathematical model used circumvents this problem by sub-dividing the lamellar unit into a thin lamella, thick lamella, transition zone between them, and the recently observed back-flip lamella. Each of these is regarded as having orthotropic symmetry. After the calculation of their Young's moduli they are rotated in space in accordance with the rotated plywood model, and then the segments are combined to present the overall modulus values in three-dimensions. The calculated trends compare well with the trends in microhardness values measured for circumferential lamellar bone. Microhardness values are, as yet, the only measurements available for direct comparison. Although the model is not directly applicable to osteonal bone, which is composed of many hollow cylinders of lamellar bone, the range of calculated modulus values and the trends observed for off-axis calculations, compare well with measured values. © 1998 Chapman & Hall     

Optical computing: introduction by the feature editors

This feature issue of Applied Optics: Information Processing contains 19 papers on Optical Computing. Many of these papers are expanded versions of presentations given at the Optical Society of America's Sixth Topical Meeting on Optical Computing held in Salt Lake City, Utah, in March 1995. This introduction provides a brief historical account of the series of optical computing meetings and a brief review of the papers contained in this special issue.     

Photorefractive processing for large adaptive phased arrays

An adaptive null-steering phased-array optical processor that utilizes a photorefractive crystal to time integrate the adaptive weights and null out correlated jammers is described. This is a beam-steering processor in which the temporal waveform of the desired signal is known but the look direction is not. The processor computes the angle(s) of arrival of the desired signal and steers the array to look in that direction while rotating the nulls of the antenna pattern toward any narrow-band jammers that may be present. We have experimentally demonstrated a simplified version of this adaptive phased-array-radar processor that nulls out the narrow-band jammers by using feedback-correlation detection. In this processor it is assumed that we know a priori only that the signal is broadband and the jammers are narrow band. These are examples of a class of optical processors that use the angular selectivity of volume holograms to form the nulls and look directions in an adaptive phased-array-radar pattern and thereby to harness the computational abilities of three-dimensional parallelism in the volume of photorefractive crystals. The development of this processing in volume holographic system has led to a new algorithm for phased-array-radar processing that uses fewer tapped-delay lines than does the classic time-domain beam former. The optical implementation of the new algorithm has the further advantage of utilization of a single photorefractive crystal to implement as many as a million adaptive weights, allowing the radar system to scale to large size with no increase in processing hardware.     

Carbohydrate chromatography: towards yoctomole sensitivity

Ward atmosphere was assessed with the help of the Ward Atmosphere Scale (WAS) by patients, nurses and physicians on 8 admission units before and after the introduction of the partial open door system. On average, on 45% of the days on which the units could have been potentially open, they were indeed at least temporarily open. Several significant changes in the WAS scores, mostly in desirable direction, were registered. However, close relationship with the partial opening of the units could be substantiated only with regard to the scale "praxis orientation"; still a positive result. No unequivocal relationships could be demonstrated between the introduction of the partial open door system and the changes in the frequency of "special events".     

A Bayesian network approach to assess and predict software quality using activity-based quality models

ContextSoftware quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty.ObjectiveThe qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model.MethodThe four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed.ResultsThe approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions.ConclusionThe approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.     

Constant-phase-mode operation of the light-addressable potentiometric sensor

The constant-phase-mode operation of the light-addressable potentiometric sensor (LAPS) is proposed and demonstrated. In this new operation mode, the temporal change and the spatial distribution of the analyte concentration are recorded in the form of the bias voltage applied to the LAPS sensor plate, which is servo-controlled to maintain the phase of the photocurrent at a constant value with respect to the light modulation. The constant-phase-mode LAPS is advantageous for its wider measurement range and reduction of artifacts.