How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.     

Promoting traits into model-driven development

Traits, as sets of behaviors, can provide a good mechanism for reusability. However, they are limited in important ways and are not present in widely used programming and modeling languages and hence are not readily available for use by mainstream developers. In this paper, we add UML associations and other modeling concepts to traits and apply them to Java and C++ through model-driven development. We also extend traits with required interfaces so dependencies at the semantics level become part of their usage, rather than simple syntactic capture. All this is accomplished in Umple, a textual modeling language based upon UML that allows adding programming constructs to the model. We applied the work to two case studies. The results show that we can promote traits to the modeling level along with the improvement in flexibility and reusability.     

Pattern matching in the Hamming distance with thresholds

It has long been known that pattern matching in the Hamming distance metric can be done in time, where n is the length of the text, m is the length of the pattern, and Σ is the alphabet. The classic algorithm for this is due to Abrahamson and Kosaraju. This paper considers the following generalization, motivated by the situation where the entries in the text and pattern are analog, or distorted by additive noise, or imprecisely given for some other reason: in any alignment of the pattern with the text, two aligned symbols a and b contribute +1 to the similarity score if they differ by no more than a given threshold θ, otherwise they contribute zero. We give an time algorithm for this more general version of the problem; the classic Hamming distance matching problem is the special case of θ=0.     

Analysis of biofluids for biomarker research

While many of the developments made in high-throughput proteomics were originally applied to procaryotic and simple eucaryotic organisms, the analysis of biofluids became increasingly important as the prospect of using proteomics to discover novel biomarkers became realized. Biofluids have represented a unique challenge to proteomics as they are often present only in small amounts and, particularly in the cases of serum and plasma, can have protein concentration ranges that differ by ten orders of magnitude. While the discovery of authentic, clinically useful biomarkers in the proteomics era has been lacking, there have been a number of significant developments in the ability to more comprehensively characterize biofluid proteomes. The rapid pace of these developments suggest that the eventual discovery of clinically validate biomarkers using proteomic technologies still has a bright future.     

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Fringe—A Java-based finite fringe analysis package

A package for analysing two-dimensional finite fringe interferograms is described. Through a combination of automatic and interactive routines, an interferogram can be processed to extract the phase shift imparted on the recording light by a transparent object. The package consists of routines to condition and pad the original image for Fourier transform analysis, to filter the image and obtain the phase, to unwrap the phase, and to remove the background phase ramp. A sample image recorded using holographic interferometry is successfully analysed.Program summaryProgram title: FRINGECatalogue identifier: AEMM_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMM_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 134006No. of bytes in distributed program, including test data, etc.: 4029801Distribution format: tar.gzProgramming language: Java.Computer: Personal Computers.Operating system: Mac OS X, Windows XP, Linux and any other system that can run Java Jar files.RAM: 1GB recommendedClassification: 18.Nature of problem: A standalone multi-platform program to perform analysis of finite fringe interferograms.Solution method: Fourier filtering approach with phase unwrapping and background subtraction.Restrictions: Designed to analyse square images.Running time: Interactive processing takes several minutes. Minimal cpu time.     

The stress dependence of the subgrain size in aluminium

Etch-pit (EP) technique and transmission electron microscopy (TEM) have been used to investigate the subgrain size, , as a function of applied shear stress, , during high-temperature creep of aluminium. Examination of thin foils, prepared from deformed specimens, in the electron microscope shows the presence of very large equiaxed sub-grains that approximate those observed in etch-pit photographs. By measuring the average subgrain size from transmission micrographs of representative areas of the foils, two observations are made. First, the average subgrain size is smaller than that determined from etch-pit procedure, but exhibits the same stress dependence: /b (/G)^– where b is the Burgers vector and G is the shear modulus. Second, the TEM subgrain size data of aluminium and those of other metals and alloys, when plotted in the normalized form of /Gb against /Gb on a logarithmic scale, where is the stacking-fault energy of the material, fall within a narrow, horizontal band, confirming earlier reports that the subgrain size is insensitive to stacking-fault energy.     

Quantitative characterization of nanoprecipitates in irradiated low-alloy steels: advances in the application of FEG-STEM quantitative microanalysis to real materials

The characterization of the solute-enriched features (clusters or nanoprecipitates in irradiated low-alloy steels) requires extremely high spatial and elemental resolution, previously necessitating analysis using atom probe field-ion microscopy. In this investigation, field-emission gun-scanning transmission electron microscope (FEG-STEM) quantitative energy dispersive X-ray (EDX) microanalysis (spectrum imaging) has been applied to the characterization of the irradiation-induced nanoprecipitates in a low-alloy forging steel. Refinements in the EDX data have been possible via the application of multivariate statistical analysis (MSA) to the spectrum images, resulting in significantly reduced noise in the images. Most importantly, MSA permitted the clear identification of other elements in these Ni-enriched nanoprecipitates—including Mn and Cu. The processed X-ray spectrum images also provided direct evidence of the preferential formation of these irradiation-induced features along pre-existing dislocations within the steel, as well as the formation of intragranular nanoprecipitates. This research has provided the first direct X-ray spectrum images of irradiation-induced nanoprecipitates in high Ni A508 Gr4N forging steel, and has demonstrated the significant improvements attainable though the application of MSA techniques to the spectrum images. These results independently confirmed the analyses of the Ni-enriched nanoprecipitates previously conducted by 3D-APFIM, with the performance of the FEG-STEM/EDX technique shown to be comparable to that of the 3D-APFIM technique.