Refined analysis of Ω−Ωˉ+\Omega^{-} \bar{\Omega}^{+} polarization in electron-positron annihilation process

We investigate the production of spin-3/2 hyperon pairs, $\Omega^- \bar{\Omega}^+$, 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 $\Omega^- \bar{\Omega}^+$ pairs. We present three sets of expressions to describe the decay of $\Omega^{-}$ hyperons, and further address the existing tension in the measurements of its decay parameters, particularly $\phi_{\Omega}$. 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