Neighborhood Homogeneous Labelings of Graphs

Given a labeling of the vertices and edges of a graph, we define a type of homogeneity that requires that the neighborhood of every vertex contains the same number of each of the labels. This homogeneity constraint is a generalization of regularity – all such graphs are regular. We consider a specific condition in which both the edge and vertex label sets have two elements and every neighborhood contains two of each label. We show that vertex homogeneity implies edge homogeneity (so long as the number of edges in any neighborhood is four), and give two theorems describing how to build new homogeneous graphs (or multigraphs) from others. Keywords: vertex labeling; edge labeling; homogenous graph; regular graph 1

Germline bias dictates cross-serotype reactivity in a common dengue-virus-specific CD8(+) T cell response.

Adaptive immune responses protect against infection with dengue virus (DENV), yet cross-reactivity with distinct serotypes can precipitate life-threatening clinical disease. We found that clonotypes expressing the T cell antigen receptor (TCR) β-chain variable region 11 (TRBV11-2) were 'preferentially' activated and mobilized within immunodominant human-leukocyte-antigen-(HLA)-A*11:01-restricted CD8(+) T cell populations specific for variants of the nonstructural protein epitope NS3133 that characterize the serotypes DENV1, DENV3 and DENV4. In contrast, the NS3133-DENV2-specific repertoire was largely devoid of such TCRs. Structural analysis of a representative TRBV11-2(+) TCR demonstrated that cross-serotype reactivity was governed by unique interplay between the variable antigenic determinant and germline-encoded residues in the second β-chain complementarity-determining region (CDR2β). Extensive mutagenesis studies of three distinct TRBV11-2(+) TCRs further confirmed that antigen recognition was dependent on key contacts between the serotype-defined peptide and discrete residues in the CDR2β loop. Collectively, these data reveal an innate-like mode of epitope recognition with potential implications for the outcome of sequential exposure to heterologous DENVs

Pattern Recognition Society Published by Elsevier Science Ltd Printed in Great Britain PII: S0031-3203(96)00173-2 AN ANALYSIS OF LOCAL FEATURE EXTRACTION IN DIGITAL MAMMOGRAPHY

Abstract--A fundamental problem of automating the detection and recognition of abnormalities in digital mammograms utilizing computational statistics is one of extracting the appropriate features for use in a classification system. Several feature sets have been proposed although none have been shown to be sufficient for the problem. Many of these features tend to be local in nature, which means their calculation requires a connected region of the image over which an average or other statistic is extracted. The implicit assumption is that the region is homogeneous, but this is rarely the case if a fixed window is used for the calculation. We consider a method of using boundaries to segment the window into more homogeneous regions for use in the feature extraction calculation. This approach is applied to the problem of discriminating between tumor and healthy tissue in digital mammography. A set of 21 images, each containing a biopsied mass, is described. The results of the boundary-gated feature extraction methodology on this image set shows a difference in distribution between tissue interior to the mass and tissue far away from the mass. Less difference is discernible when boundaries are not used in the feature extraction. Published by Elsevier Science Ltd. Image processing Feature extraction Local features Boundary gating 1

Manifold matching: Joint optimization of fidelity and commensurability

Abstract. Fusion and inference from multiple and massive disparate data sources—the requirement for our most challenging data analysis problems and the goal of our most ambitious statistical pattern recognition methodologies—has many and varied aspects which are currently the target of intense research and development. One aspect of the overall challenge is manifold matching—identifying embeddings of multiple disparate data spaces into the same low-dimensional space where joint inference can be pursued. We investigate this manifold matching task from the perspective of jointly optimizing the fidelity of the embeddings and their commensurability with one another, with a specific statistical inference exploitation task in mind. Our results demonstrate when and why our joint optimization methodology is superior to either version of separate optimization. The methodology is illustrated with simulations and an application in document matching

Journal of Statistical Planning and

journal of statistical planning and inference Semiparametric nonhomogeneity analysis

On the Minimization of Concave Information Functionals for Unsupervised Classification via Decision Trees Abstract

A popular method for unsupervised classification of high-dimensional data via decision trees is characterized as minimizing the empirical estimate of a concave information functional. It is shown that minimization of such functionals under the true distributions leads to perfect classification. Key words: Decision trees, clustering, unsupervised classification, information functionals, disjoint supports