Structure and stability of quasi-two-dimensional boson-fermion mixtures with vortex-antivortex superposed states

We investigate the equilibrium properties of a quasi-two-dimensional degenerate boson-fermion mixture (DBFM) with a bosonic vortex-antivortex superposed state (VAVSS) using a quantum-hydrodynamic model. We show that, depending on the choice of parameters, the DBFM with a VAVSS can exhibit rich phase structures. For repulsive boson-fermion (BF) interaction, the Bose-Einstein condensate (BEC) may constitute a petal-shaped "core" inside the honeycomb-like fermionic component, or a ring-shaped joint "shell" around the onion-like fermionic cloud, or multiple segregated "islands" embedded in the disc-shaped Fermi gas. For attractive BF interaction just below the threshold for collapse, an almost complete mixing between the bosonic and fermionic components is formed, where the fermionic component tends to mimic a bosonic VAVSS. The influence of an anharmonic trap on the density distributions of the DBFM with a bosonic VAVSS is discussed. In addition, a stability region for different cases of DBFM (without vortex, with a bosonic vortex, and with a bosonic VAVSS) with specific parameters is given.Comment: 8 pages,5 figure

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi

The gravitational time delay in the field of a slowly moving body with arbitrary multipoles

We calculate the time delay of light in the gravitational field of a slowly moving body with arbitrary multipoles (mass and spin multipole moments) by the Time-Transfer-Function (TTF) formalism. The parameters we use, first introduced by Kopeikin for a gravitational source at rest, make the integration of the TTF very elegant and simple. Results completely coincide with expressions from the literature. The results for a moving body (with constant velocity) with complete multipole-structure are new, according to our knowledge.Comment: 9 pages, no figure