ORIGO stepping stones common core mathematics Grade 5 practice book

ORIGO Stepping Stones is written and developed by a team of experts to provide teachers with a world-class elementary math program. Our expert team of authors and consultants are utilizing all available educational research to create a unique program that has never before been available to teachers. The full color Student Practice Book provides practice pages that support previous and current lessons.

The relevance of shared experiences : a multi-level study of collective efficacy as a moderator of job control in the stressor-strain relationship

Karasek's Job Demand-Control model proposes that control mitigates the positive effects of work stressors on employee strain. Evidence to date remains mixed and, although a number of individual-level moderators have been examined, the role of broader, contextual, group factors has been largely overlooked. In this study, the extent to which control buffered or exacerbated the effects of demands on strain at the individual level was hypothesized to be influenced by perceptions of collective efficacy at the group level. Data from 544 employees in Australian organizations, nested within 23 workgroups, revealed significant three-way cross-level interactions among demands, control and collective efficacy on anxiety and job satisfaction. When the group perceived high levels of collective efficacy, high control buffered the negative consequences of high demands on anxiety and satisfaction. Conversely, when the group perceived low levels of collective efficacy, high control exacerbated the negative consequences of high demands on anxiety, but not satisfaction. In addition, a stress-exacerbating effect for high demands on anxiety and satisfaction was found when there was a mismatch between collective efficacy and control (i.e. combined high collective efficacy and low control). These results provide support for the notion that the stressor-strain relationship is moderated by both individual- and group-level factors.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Differential expression of NADPH-diaphorase between electrophysiologically-defined classes of pyramidal neurons in rat ventral subiculum, in vitro

The subiculum is the major output region of the hippocampal formation. We have studied pyramidal neurons in slices of rat ventral subiculum to determine if there is a correlation between nicotinamide adenine dinucleotide phosphate-diaphorase (NADPH-d) activity and electrophysiological phenotype. The majority of NADPH-d-positive pyramidal neurons were found in the superficial cell layer (i.e. nearest to the hippocampal fissure) of the subiculum and appreciable NADPH-d activity was absent from pyramidal neurons in area CA1. This distribution of NADPH-d activity was mimicked by that of immunoreactivity for the neuronal isoform of nitric oxide synthase. Subicular pyramidal neurons were classified, electrophysiologically, as intrinsically burst-firing or regular spiking. After electrophysiological characterization, neurons were filled with Neurobiotin and revealed using fluorescence immunocytochemistry. The slices containing these neurons were also processed for NADPH-d. NADPH-d activity was found in six out of eight regular spiking neurons but was not found in any of 13 intrinsically burst-firing neurons (P=0.0008, Fisher's Exact Test). We conclude that in rat ventral subiculum, NADPH-d activity is present in a proportion of pyramidal neurons and indicates the presence of the neuronal isoform of nitric oxide synthase. Furthermore, amongst pyramidal neurons, NADPH-d activity is distributed preferentially to those with the regular spiking phenotype. The distribution of regular spiking neurons suggests that they may not be present to the same extent in all subicular output pathways. Thus, the actions of nitric oxide may be relatively specific to particular hippocampal connections.

Validity of the previous day physical activity recall (PDPAR) in fifth-grade children

This study evaluated the validity of the Previous Day Physical Activity Recall (PDPAR) self-report instrument in quantifying after-school physical activity behavior in fifth-grade children. Thirty-eight fifth-grade students (mean age, 10.8 +/- 0.1; 52.6% female; 26.3% African American) from two urban elementary schools completed the PDPAR after wearing a CSA WAM 7164 accelerometer for a day. The mean within-subject correlation between self-reported MET level and total counts for each 30-min block was 0.57 (95% C.I., 0.51-0.62). Self-reported mean MET level during the after-school period and the number of 30-min blocks with activity rated at greater than or equal to 6 METs were significantly correlated with the CSA outcome variables. Validity coefficients for these variables ranged from 0.35 to 0.43 (p <.05). Correlations between the number of 30-min blocks with activity rated at greater than or equal to 3 METs and the CSA variables were positive but failed to reach statistical significance (r = 0.19-0.23). The PDPAR provides moderately valid estimates of relative participation in vigorous activity and mean MET level in fifth-grade children. Caution should be exercised when using the PDPAR to quantify moderate physical activity in preadolescent children.

A prospective study of the determinants of physical activity in rural fifth-grade children

Background Understanding the factors that influence physical activity behavior is important in the design of intervention programs targeted at youth. Methods A prospective study design was used to identify the predictors of vigorous physical activity (VPA) (greater than or equal to 6 METs) and moderate and vigorous physical activity (MVPA) (greater than or equal to 3 METs) among 202 rural, predominantly African-American children. Selected social-cognitive determinants of physical activity were assessed via questionnaire in the fifth grade. Participation in VPA and MVPA was assessed via the previous day physical activity recall 1 year later in the sixth grade. Results For girls, participation in community sports, self-efficacy in overcoming barriers, enjoyment of school physical education, race (white > black), and perception of mother's activity level (active vs inactive) were significant predictors of VPA. For MVPA, participation in community sports and self-efficacy in overcoming barriers were significant predictors. For boys, self-efficacy in overcoming barriers was the only significant predictor of VPA, while beliefs regarding activity outcomes and participation in community sports were significant predictors of MVPA. Conclusion Social-cognitive constructs such as physical activity self-efficacy, access to community physical activity outlets, and positive beliefs regarding physical activity outcomes are important predictors of future physical activity behavior among rural, predominantly African-American children.