77,856 research outputs found
Motivation in physical education across the primary-secondary school transition
The purpose of this study was to examine the temporal patterns of approach-avoidance achievement goals, implicit theories of ability and perceived competence in physical education across the transition from primary to secondary school. We also evaluated the predictive utility of implicit theories and perceived competence with regard to achievement goal adoption, and determined the moderating influence of gender on temporal patterns and antecedent–goal relationships. One hundred and forty pupils (mean age at start of study = 11.37 years, SD =.28) completed measures of entity and incremental beliefs, perceived competence and goals on four occasions during a 12-month period. Mastery-approach, performance-approach and perform-ance-avoidance goals, as well as entity and incremental beliefs, exhibited a linear decline over time. Mastery-avoidance goals showed no significant change. Girls exhibited a linear decline in perceived competence, whereas for boys the trajectory was curvilinear. Competence perceptions predicted initial scores, but not rate of change, on mastery-approach and both types of performance goals. Incrementa
The role of Intangible Assets in the Relationship between HRM and Innovation: A Theoretical and Empirical Exploration
This paper, as far as known, provides a first attempt to explore the role of intellectual capital (IC) and knowledge management (KM) in an integrative way between the relationship of human resource (HR) practices and two types of innovation (radical and incremental). More specifically, the study investigates two sub-components of IC – human capital and organizational social capital. At the same time, four KM channels are discussed, such as knowledge creation, acquisition, transfer and responsiveness.\ud
The research is a part of a bigger project financed by the Ministry of Economic Affairs and the province of Overijssel in the Netherlands. The project studies the ‘competencies for innovation’ and is conducted in collaboration with innovative companies in the Eastern part of the Netherlands. \ud
An exploratory survey design with qualitative and quantitative data is used for\ud
investigating the topic in six companies from industrial and service sector in the region of Twente, the Netherlands. Mostly, the respondents were HR directors. The findings showed that some parts of IC and KM configurations were related to different types of innovation. To make the picture even more complicated, HR practices were sometimes perceived interchangeably with IC and KM by HR directors. Overall, the whole picture about the relationships stays unclear and opens a floor for further research
GPU Based Path Integral Control with Learned Dynamics
We present an algorithm which combines recent advances in model based path
integral control with machine learning approaches to learning forward dynamics
models. We take advantage of the parallel computing power of a GPU to quickly
take a massive number of samples from a learned probabilistic dynamics model,
which we use to approximate the path integral form of the optimal control. The
resulting algorithm runs in a receding-horizon fashion in realtime, and is
subject to no restrictive assumptions about costs, constraints, or dynamics. A
simple change to the path integral control formulation allows the algorithm to
take model uncertainty into account during planning, and we demonstrate its
performance on a quadrotor navigation task. In addition to this novel
adaptation of path integral control, this is the first time that a
receding-horizon implementation of iterative path integral control has been run
on a real system.Comment: 6 pages, NIPS 2014 - Autonomously Learning Robots Worksho
Nonparametric causal effects based on incremental propensity score interventions
Most work in causal inference considers deterministic interventions that set
each unit's treatment to some fixed value. However, under positivity violations
these interventions can lead to non-identification, inefficiency, and effects
with little practical relevance. Further, corresponding effects in longitudinal
studies are highly sensitive to the curse of dimensionality, resulting in
widespread use of unrealistic parametric models. We propose a novel solution to
these problems: incremental interventions that shift propensity score values
rather than set treatments to fixed values. Incremental interventions have
several crucial advantages. First, they avoid positivity assumptions entirely.
Second, they require no parametric assumptions and yet still admit a simple
characterization of longitudinal effects, independent of the number of
timepoints. For example, they allow longitudinal effects to be visualized with
a single curve instead of lists of coefficients. After characterizing these
incremental interventions and giving identifying conditions for corresponding
effects, we also develop general efficiency theory, propose efficient
nonparametric estimators that can attain fast convergence rates even when
incorporating flexible machine learning, and propose a bootstrap-based
confidence band and simultaneous test of no treatment effect. Finally we
explore finite-sample performance via simulation, and apply the methods to
study time-varying sociological effects of incarceration on entry into
marriage
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
An ant colony optimization (ACO) algorithm offers
algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution
constructions and to realize a pheromone laying-and-following
mechanism. Although ACO is first designed for solving discrete
(combinatorial) optimization problems, the ACO procedure is
also applicable to continuous optimization. This paper presents
a new way of extending ACO to solving continuous optimization
problems by focusing on continuous variable sampling as a key
to transforming ACO from discrete optimization to continuous
optimization. The proposed SamACO algorithm consists of three
major steps, i.e., the generation of candidate variable values for
selection, the ants’ solution construction, and the pheromone
update process. The distinct characteristics of SamACO are the
cooperation of a novel sampling method for discretizing the
continuous search space and an efficient incremental solution
construction method based on the sampled values. The performance
of SamACO is tested using continuous numerical functions
with unimodal and multimodal features. Compared with some
state-of-the-art algorithms, including traditional ant-based algorithms
and representative computational intelligence algorithms
for continuous optimization, the performance of SamACO is seen
competitive and promising
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