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The problem of body fitting meshes that are both adaptive and geometrically accurate is important in a variety of biomedical applications in a multitude of clinical settings, including electrocardiology, neurology, and orthopedics. Adaptivity is necessary because of the combination of large-scale and smallscale structures (e.g. relatively small blood vessels spanning a human head). Geometric accuracy is important for several reasons. In some cases, such as computational fluid dynamics, the fine-scale structure of the fluid domain is important for qualitative and quantitative accuracy of the solutions. More generally, finite element approximations of elliptic problems with rough coefficients require increased spatial resolution normal to material boundaries [3]. The problem of constructing meshes from biomedical images is particularly difficult because of the complexity and irregularity of the structures, and thus tuning or correcting meshes by hand is quite difficult and time consuming. Many researchers and, indeed, commercial products simply subdivide the underlying hexahedral image grid and assign material properties to tetrahedra based on standard decomposition of each hexahedron into tetrahedra.

This paper presents a small case study of the results of a recently developed method for multimaterial, tetrahedral meshing of biomedical volumes [6]. The method uses an iterative relaxation of surface point point positions that are constrained to subsets of the volume that correspond to boundaries between different materials. In this paper we briefly review the method and present results on a set of MRI head images for use in bioelectric field simulation and source localization.

Numerical integration formulas in n-dimensional nonsymmetric Euclidean space of degree two, consisting of n+1 equally weighted points, are discussed, for a class of integrals often encountered in statistics. This is an extension of Stroud's theory [A.H. Stroud, Remarks on the disposition of points in numerical integration formulas, Math. Comput. 11 (60) (1957) 257–261; A.H. Stroud, Numerical integration formulas of degree two, Math. Comput. 14 (69) (1960) 21–26]. Explicit formulas are given for integrals with nonsymmetric weights. These appear to be new results and include the Stroud's degree two formula as a special case.

Keywords: Numerical integration, Cubature formulas

The topic of studying the growth of human brain development has become of increasing interests in the neuroimaging community. Cross-sectional studies may allow comparisons between means of different age groups, but they do not provide any growth model that integrates the continuum of time, nor do they present any information about how individuals/population change over time. Longitudinal data analysis method arises as a strong tool to address these questions. In this paper, we use longitudinal analysis methods to study tissue development in early brain growth; a novel approach of multivariate longitudinal analysis is applied to study the associations between the growth of different brain tissues. We present in this paper the methodologies to statistically study scalar (univariate) and vector (multivariate) longitudinal data, and our exploratory results in the study of neonatal-pediatric brain tissue development. We obtained growth curves as a quadratic function of time for all three tissues. The quadratic terms were then tested to be statistically signicant, showing that there was indeed a quadratic growth of tissues in early brain development. Moreover, our result shows that there is a positive correlation between repeated measurements of any single tissue, and among those of different tissues. Our approach is generic in natural and thus can be applied to any longitudinal data with multiple outcomes, even brain structures. Also, our joint mixed model is flexible enough to allow incomplete and unbalanced data, i.e. subjects do not need to have the same number of measurements, or be measured at the exact time points.

Keywords: ucnia

In applying stateless model checking methods to realistic multithreaded programs, we find that stateless search methods are ineffective in practice, even with dynamic partial order reduction (DPOR) enabled. To solve the inefficiency of stateless runtime model checking, this paper makes two related contributions. The first contribution is a novel and conservative light-weight method for storing abstract states at runtime to help avoid redundant searches. The second contribution is a stateful dynamic partial order reduction algorithm (SDPOR) that avoids a potential unsoundness when DPOR is naively applied in the context of stateful search. Our stateful runtime model checking approach combines light-weight state recording with SDPOR, and strikes a good balance between state recording overheads, on one hand, and the elimination of redundant searches, on the other hand. Our experiments confirm the effectiveness of our approach on several multithreaded benchmarks in C, including some practical programs.