Limitations on the uses of multimedia exposure measurements for multipathway exposure assessment--Part II: Effects of missing data and imprecision.

Multimedia data from two probability-based exposure studies were investigated in terms of how missing data and measurement-error imprecision affected estimation of population parameters and associations. Missing data resulted mainly from individuals'refusing to participate in certain measurement activities, rather than from field or laboratory problems; it suggests that future studies should focus on methods for maximizing participation rates. Measurement error variances computed from duplicate-sample data were small relative to the inherent variation in the populations; consequently, adjustments in nonparametric percentile estimates to account for measurement imprecision were small. Methods of adjustment based on lognormality assumptions, however, appeared to perform poorly.     

Midplane-symmetry breaking in the flow between two counter-rotating disks

This paper considers the axisymmetric steady flow driven by exact counter rotation of two co-axial disks of finite radius. At the edges of the rotating disks one of three conditions is (typically) imposed: (i) zero velocity, corresponding to a stationary, impermeable, cylindrical shroud (ii) zero normal velocity and zero tangential fluid traction, corresponding to a (confined) free surface and (iii) an edge constraint that is consistent with a similarity solution of von Kármán form. The similarity solution is valid in an infinite geometry and possesses a pitchfork bifurcation that breaks the midplane symmetry at a critical Reynolds number. In this paper, similar bifurcations of the global (finite-domain) flow are sought and comparisons are made between the resulting bifurcation structure and that found for the similarity solution. The aim is to assess the validity of the nonlinear similarity solutions in finite domains and to explore the sensitivity of the solution structure to edge conditions that are implicitly neglected when assuming a self-similar flow. It is found that, whilst the symmetric similarity solution can be quantitatively useful for a range of boundary conditions, the bifurcated structure of the finite-domain flow is rather different for each boundary condition and bears little resemblance to the self-similar flow.     

The accuracy of self-monitoring and its relationship to self-focused attention in dysphoria and clinical depression.

The accuracy with which dysphoric (Study 1) and clinically depressed (Study 2) individuals make self-regulatory judgments about their own performance in the absence of external feedback and the extent to which this relates to trait self-focused attention (SFA) were examined. Relative to objective criteria, both dysphoric and depressed participants showed a positive judgment bias, overestimating the number of trials they had performed correctly. Relative to control participants, the dysphoric and depressed groups showed a reduction in the extent of this positive bias in that they judged error trials more accurately and correct trials less accurately. Although the dysphoric and depressed groups both reported elevated trait SFA, this did not correlate significantly with accuracy of self-judgment on the performance-monitoring task. Implications for self-regulation models of depression are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Lambda Library: unnamed functions in C++

The Lambda Library (LL) adds a form of lambda functions to C++, which are common in functional programming languages. The LL is implemented as a template library using standard C++; thus no language extensions or preprocessing is required. The LL consists of a rich set of tools for defining unnamed functions. In particular these unnamed functions work seamlessly with the generic algorithms in the C++ Standard Library. The LL offers significant improvements, in terms of generality and ease of use, compared to the current tools in the C++ Standard Library. Copyright © 2003 John Wiley & Sons, Ltd.     

Locally Weighted Learning

This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.