12,805 research outputs found
Refined analysis of polarization in electron-positron annihilation process
We investigate the production of spin-3/2 hyperon pairs, , in electron-positron annihilation within the helicity
amplitude formalism. A refined selection of helicity basis matrices is proposed
to relate polarization expansion coefficients and spin density matrix elements
and to illuminate their inherent physical interpretations and symmetrical
properties. With a novel parametrization scheme of helicity amplitudes, we
perform an analysis of polarization correlation coefficients for double-tag
pairs. We present three sets of expressions to
describe the decay of hyperons, and further address the existing
tension in the measurements of its decay parameters, particularly
. The method and the framework developed in this paper can also
be applied to studies of the production and decay mechanisms of other spin-3/2
particles.Comment: 43 pages, 4 figure
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
Tetrakis(nitrato-κ2 O,O′)[N,N′-1,4-phenylenebis(pyridine-4-carboxamide)-κN 1](4-{[4-(pyridine-4-carboxamido-κN 1)phenyl]carbamoyl}pyridin-1-ium)neodymium(III)
In the title compound, [Nd(NO3)4(C18H15N4O2)(C18H14N4O2)], the NdIII centre is located on a twofold axis and exhibits a ten-coordinated distorted bicapped square-antiprismatic geometry. The pyridinium NH H atom is disordered over the two ligands. Adjacent mononuclear clusters are linked through N—H⋯O and N—H⋯N hydrogen-bonding interactions, generating layers in the (102) plane
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