Improving Person Re-identification by Attribute and Identity Learning

Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.Comment: Accepted to Pattern Recognition (PR

Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs

Spin-orbit-coupling-induced magnetic heterostructure in the bilayer Bose-Hubbard system

We investigate magnetic phase in the bilayer system of ultra-cold bosons in an optical lattice, which is involved with Raman-induced spin-orbit (SO) coupling and laser-assisted interlayer tunneling. It is shown that there exit a rich of spin textures such as hetero ferromagnet, heterochiral magnet, chiral magnet with interlayer antiferromagnet. In particular, heterochiral magnet induced by SO coupling occurs extremely rarely in real solid-state materials. We present detailed experimental setup of realizing such a model in cold atom system.Comment: 7 pages of RevTex4-1, 4 figure

OBCS: The Ontology of Biological and Clinical Statistics

Statistics play a critical role in biological and clinical research. To promote logically consistent representation and classification of statistical entities, we have developed the Ontology of Biological and Clinical Statistics (OBCS). OBCS extends the Ontology of Biomedical Investigations (OBI), an OBO Foundry ontology supported by some 20 communities. Currently, OBCS contains 686 terms, including 381 classes imported from OBI and 147 classes specific to OBCS. The goal of this paper is to present OBCS for community critique and to describe a number of use cases designed to illustrate its potential applications. The OBCS project and source code are available at http://obcs.googlecode.com

Photonic Bloch-dipole-Zener Oscillations in Binary Parabolic Optical Waveguide Arrays

We have studied the propagation and Zener tunneling of light in the binary parabolic optical waveguide array (BPOWA), which consists of two evanescently coupled dissimilar optical waveguides. Due to Bragg reflections, BPOWA attains two minibands separated by a minigap at the zone boundary. Various coherent superpositions of optical oscillations and Zener tunneling occur for different parameters on the phase diagram. In particular, Bloch-Zener oscillation and a different type of Bloch-dipole-Zener oscillation are obtained by the field-evolution analysis. The results may have potential applications in optical splitting and waveguiding devices and shed light on the coherent phenomena in optical lattices.Comment: Submitted to JOSA