Family Caregivers of Stroke Survivors: Characteristics of Caregivers at Risk for Depression.

Objective: To examine characteristics that best predict family caregivers of stroke survivors at risk for depression. Survivor and caregiver demographic variables and caregiver general health, problem-solving skills, social support, satisfaction with rehabilitation health care services, preparedness, and burden were examined. Design: A correlational design. Participants: Seventy-four family caregivers of individuals receiving inpatient stroke rehabilitation were interviewed 1-2 days prior to their relative's discharge. Main Outcome Measure: The Center for Epidemiologic Studies Depression Scale (L. S. Radloff. 1977). Results: Caregivers at risk for depression had a negative orientation toward solving problems, a lack of caregiver preparedness, and impaired social functioning secondary to their own health. Conclusions: Rehabilitation psychologists should be aware that these variables are important in predicting family caregivers of stroke survivors at risk for depression. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Robust reliable control for a class of uncertain nonlinear systems with time-varying multistate time delays

This paper deals with the problem of robust reliable controller design for a class of uncertain nonlinear systems. The system under consideration is subject to time-varying multistate time delays, parameter uncertainties, nonlinearities and even failures in prescribed subsets of auctuators. The nonlinearities are assumed to satisfy boundedness conditions. The parameter uncertainties and multistate time delays are time varying and structured. A systematic approach using linear matrix inequalities (LMIs) is proposed to solve the design problem of this controller. Sufficient conditions for robust stability in the presence of possible actuator failure are given in terms of the LMIs and the controller that stabilizes the system is computed using standard LMI techniques.     

Continental scale hotspot observations of Australia at sub-degree angular resolution from POLDER

Angular signatures of the innermost few degrees of the hotspot are retrieved on Australian land surfaces with an angular resolution better than 0.5° and a spatial resolution of around 150?km, using data from the Polarization and Directionality of the Earth's Reflectances (POLDER) instrument on the Advanced Earth Observing Satellite (ADEOS). These constitute the first comprehensive hotspot measurements at the scale of a continent. The retrieval technique requires that the hotspot characteristics at small angular scales are approximately invariant over a region of about 50?km in extent, but accommodates pixel-to-pixel variations in the other characteristics of the angular signature. Continental maps of the amplitude and half-width of the hotspot, in two spectral bands, are presented for two 41-day orbit cycles in different seasons and compared with a vegetation map. The half-widths are predominantly in the range 0.5°–1.5°, with no systematic difference between the 670?nm and 865?nm bands, and show little spatial structure. The maps of hotspot amplitude display significant spatial structure that corresponds closely to the vegetation map. Generally, areas of trees and tall shrubs are characterized by low hotspot amplitudes, while cropland, grassland and areas of low shrubs have high hotspot amplitudes. The evergreen broadleaf eucalypt forests are notable in that the amplitude is low at 670?nm and high at 865?nm.     

Toward a Model for Backtracking and Dynamic Programming

We propose a model called priority branching trees (pBT) for backtracking and dynamic programming algorithms. Our model generalizes both the priority model of Borodin, Nielson and Rackoff, as well as a simple dynamic programming model due to Woeginger, and hence spans a wide spectrum of algorithms. After witnessing the strength of the model, we then show its limitations by providing lower bounds for algorithms in this model for several classical problems such as Interval Scheduling, Knapsack and Satisfiability.     

Spatially-Structured Sharing Technique for Multimodal Problems

Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm （EA）. However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone （and vice-versa）.     

Causes and reasons in attribution theory: A conceptual critique.

Argues that although the terms cause and reason have both appeared in the attribution theory literature, they have not been adequately distinguished from each other. With the aid of some recent ideas from the philosophy of mind on the cause–reason distinction, a conceptual critique is undertaken of the way these terms have been used by attribution theorists. Two topics are considered: (a) E. E. Jones and R. E. Nisbett's (1972) ideas on actor–observer differences in causal attributions and (b) A. W. Kruglanski's (1975) criticisms of the internal–external distinction and his own endogenous–exogenous reformulation. The conceptual critique ends in a taxonomy of attributions and a plea for greater precision in the use of the terms cause and reason in this area. (37 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Linear and kernel methods for multivariate change detection

The iteratively reweighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsupervised change detection in multi- and hyperspectral remote sensing imagery and for automatic radiometric normalization of multitemporal image sequences. Principal components analysis (PCA), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving an order of magnitude enhancement in computational speed. The software is available from the authors' Web sites.     

Dual transmission model and related spectral content of the fetal heart sounds

A dual transmission model of the fetal heart sounds is presented in which the properties of the signals received on a sensor, installed on the maternal abdominal surface, depend upon the position of the fetus. For a fetus in the occiput anterior position, the predominant spectral content lies in the frequency band 16-50Hz ("impact" mode), but for a fetus in the occiput posterior position, it lies in the frequency band 80-110Hz ("acoustic" mode). Signal processing comprises digital bandpass filtering, matched filtering, Teager energy operator, autocorrelation, and figure of merit algorithms. The digital filter permits the user to select the frequency band that best conforms to the prevailing signal mode. Clinical tests on twelve patients, with some in the occiput anterior and some in the occiput posterior fetal positions, support the validity of the dual transmission model.     

A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching

We present a numerical algorithm for a new matching approach for parameterisation independent diffeomorphic registration of curves in the plane, targeted at robust registration between curves that require large deformations. This condition is particularly useful for the geodesic constrained approach in which the matching functional is minimised subject to the constraint that the evolving diffeomorphism satisfies the Hamiltonian equations of motion; this means that each iteration of the nonlinear optimisation algorithm produces a geodesic (up to numerical discretisation). We ensure that the computed solutions correspond to geodesics in the shape space by enforcing the horizontality condition (conjugate momentum is normal to the curve). Explicitly introducing and solving for a reparameterisation variable allows the use of a point-to-point matching condition. The equations are discretised using the variational particle-mesh method. We provide comprehensive numerical convergence tests and benchmark the algorithm in the context of large deformations, to show that it is a viable, efficient and accurate method for obtaining geodesics between curves.