9,929 research outputs found
Linking engagement and performance: The social network analysis perspective
Theories developed by Tinto and Nora identify academic performance, learning
gains, and involvement in learning communities as significant facets of student
engagement that, in turn, support student persistence. Collaborative learning
environments, such as those employed in the Modeling Instruction introductory
physics course, provide structure for student engagement by encouraging
peer-to-peer interactions. Because of the inherently social nature of
collaborative learning, we examine student interactions in the classroom using
network analysis. We use centrality---a family of measures that quantify how
connected or "central" a particular student is within the classroom
network---to study student engagement longitudinally. Bootstrapped linear
regression modeling shows that students' centrality predicts future academic
performance over and above prior GPA for three out of four centrality measures
tested. In particular, we find that closeness centrality explains 28 % more of
the variance than prior GPA alone. These results confirm that student
engagement in the classroom is critical to supporting academic performance.
Furthermore, we find that this relationship for social interactions does not
emerge until the second half of the semester, suggesting that classroom
community develops over time in a meaningful way
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
Cluster varieties from Legendrian knots
Many interesting spaces --- including all positroid strata and wild character
varieties --- are moduli of constructible sheaves on a surface with
microsupport in a Legendrian link. We show that the existence of cluster
structures on these spaces may be deduced in a uniform, systematic fashion by
constructing and taking the sheaf quantizations of a set of exact Lagrangian
fillings in correspondence with isotopy representatives whose front projections
have crossings with alternating orientations. It follows in turn that results
in cluster algebra may be used to construct and distinguish exact Lagrangian
fillings of Legendrian links in the standard contact three space.Comment: 47 page
What the council of economic advisors need to know about sustainable development
The decision by Alex Salmond to appoint a Council of Economic Advisors to move economic decision making away from purely political rationale is particularly welcome given the new administration’s commitment to sustainable economic growth as the overarching priority. From the first Minister’s statement to parliament4 is clear that as an economist he recognises that sustainable economic growth is not (just) economic growth that continues but economic growth that is environmentally and socially sustainable. In the Scottish Environment Protection Agency we have wrestled with just what sustainable economic growth might mean and here we offer some of our own thoughts to help the new council of economic advisors in their work
A Dirac Sea and thermodynamic equilibrium for the quantized three-wave interaction
The classical version of the three wave interaction models the creation and
destruction of waves; the quantized version models the creation and destruction
of particles. The quantum three wave interaction is described and the Bethe
Ansatz for the eigenfunctions is given in closed form. The Bethe equations are
derived in a rigorous fashion and are shown to have a thermodynamic limit. The
Dirac sea of negative energy states is obtained as the infinite density limit.
Finite particle/hole excitations are determined and the asymptotic relation of
energy and momentum is obtained. The Yang-Yang functional for the relative free
energy of finite density excitations is constructed and is shown to be convex
and bounded below. The equations of thermal equilibrium are obtained
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
The propagation of sound in a shallow water environment is characterized by
boundary reflections from the sea surface and sea floor. These reflections
result in multiple (indirect) sound propagation paths, which can degrade the
performance of passive sound source localization methods. This paper proposes
the use of convolutional neural networks (CNNs) for the localization of sources
of broadband acoustic radiated noise (such as motor vessels) in shallow water
multipath environments. It is shown that CNNs operating on cepstrogram and
generalized cross-correlogram inputs are able to more reliably estimate the
instantaneous range and bearing of transiting motor vessels when the source
localization performance of conventional passive ranging methods is degraded.
The ensuing improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.Comment: 5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap
with arXiv:1612.0350
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