2,539 research outputs found
Welche Konsequenzen ziehen Lehramtsstudierende aus dem Lernstand der eigenen SchülerInnen für ihre Unterrichtsplanung?
Umsetzung eines Diagnose- und Förderprozesses durch angehende Mathematiklehrpersonen im Schulpraktikum
Oestradiol moderates the association of visceral fat on brain structure and cognitive function in women
Supersymmetry-generated complex optical potentials with real spectra
We show that the formalism of supersymmetry (SUSY), when applied to
parity-time (PT) symmetric optical potentials, can give rise to novel
refractive index landscapes with altogether non-trivial properties. In
particular, we find that the presence of gain and loss allows for arbitrarily
removing bound states from the spectrum of a structure. This is in stark
contrast to the Hermitian case, where the SUSY formalism can only address the
fundamental mode of a potential. Subsequently we investigate isospectral
families of complex potentials that exhibit entirely real spectra, despite the
fact that their shapes violate PT-symmetry. Finally, the role of SUSY
transformations in the regime of spontaneously broken PT symmetry is
investigated.Comment: 6 pages, 4 figure
Sprachschichten im Mittelalter
This summary covers beaches considered to be tactically important. For full description of coastline, see section VI. Information common to all beaches: tidal range, approximately 4ft (higher high water heights to lowest tide) All depths given at mean LLW. Towns adjacent to most beaches, water supplies unknown
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201
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