Rapidly Rotating Fermi Gases

We show that the density profile of a Fermi gas in rapidly rotating potential will develop prominent features reflecting the underlying Landau level like energy spectrum. Depending on the aspect ratio of the trap, these features can be a sequence of ellipsoidal volumes or a sequence of quantized steps.Comment: 4 pages, 1 postscript fil

Zernike velocity moments for sequence-based description of moving features

The increasing interest in processing sequences of images motivates development of techniques for sequence-based object analysis and description. Accordingly, new velocity moments have been developed to allow a statistical description of both shape and associated motion through an image sequence. Through a generic framework motion information is determined using the established centralised moments, enabling statistical moments to be applied to motion based time series analysis. The translation invariant Cartesian velocity moments suffer from highly correlated descriptions due to their non-orthogonality. The new Zernike velocity moments overcome this by using orthogonal spatial descriptions through the proven orthogonal Zernike basis. Further, they are translation and scale invariant. To illustrate their benefits and application the Zernike velocity moments have been applied to gait recognition—an emergent biometric. Good recognition results have been achieved on multiple datasets using relatively few spatial and/or motion features and basic feature selection and classification techniques. The prime aim of this new technique is to allow the generation of statistical features which encode shape and motion information, with generic application capability. Applied performance analyses illustrate the properties of the Zernike velocity moments which exploit temporal correlation to improve a shape's description. It is demonstrated how the temporal correlation improves the performance of the descriptor under more generalised application scenarios, including reduced resolution imagery and occlusion

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Post-merger Signatures of Red-sequence Galaxies in Rich Abell Clusters at z≲0.1z\lesssim 0.1

We have investigated the post-merger signatures of red-sequence galaxies in rich Abell clusters at $z \lesssim$ 0.1: A119, A2670, A3330 and A389. Deep images in u', g', r' and medium-resolution galaxy spectra were taken using MOSAIC 2 CCD and Hydra MOS mounted on a Blanco 4-m telescope at CTIO. Post-merger features are identified by visual inspection based on asymmetric disturbed features, faint structures, discontinuous halo structures, rings and dust lanes. We found that ~ 25% of bright (M_r < -20) cluster red-sequence galaxies show post-merger signatures in four clusters consistently. Most (~ 71%) of the featured galaxies were found to be bulge-dominated, and for the subsample of bulge-dominated red-sequence galaxies, the post-merger fraction rises to ~ 38%. We also found that roughly 4% of bulge-dominated red-sequence galaxies interact (on-going merger). A total of 42% (38% post-merger, 4% on-going merger) of galaxies show merger-related features. Compared to a field galaxy study with a similar limiting magnitude (van Dokkum 2005), our cluster study presents a similar post-merger fraction but a markedly lower on-going merger fraction. The merger fraction derived is surprisingly high for the high density of our clusters, where the fast internal motions of galaxies are thought to play a negative role in galaxy mergers. The fraction of post-merger and on-going merger galaxies can be explained as follows. Most of the post-merger galaxies may have carried over their merger features from their previous halo environment, whereas interacting galaxies interact in the current cluster in situ. According to our semi-analytic calculation, massive cluster haloes may very well have experienced tens of halo mergers over the last 4-5 Gyr; post-merger features last that long, allowing these features to be detected in our clusters today. (Abridged)Comment: 16 pages, 15 figures, 7 tables, accepted for publication in ApJ