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

Fractional Quantum Hall Effect of Hard-Core Bosons in Topological Flat Bands

Recent proposals of topological flat band (TFB) models have provided a new route to realize the fractional quantum Hall effect (FQHE) without Landau levels. We study hard-core bosons with short-range interactions in two representative TFB models, one of which is the well known Haldane model (but with different parameters). We demonstrate that FQHE states emerge with signatures of even number of quasi-degenerate ground states on a torus and a robust spectrum gap separating these states from higher energy spectrum. We also establish quantum phase diagrams for the filling factor 1/2 and illustrate quantum phase transitions to other competing symmetry-breaking phases.Comment: 4 pages, 6 figure

Ground-state properties via machine learning quantum constraints

Ground-state properties are central to our understanding of quantum many-body systems. At first glance, it seems natural and essential to obtain the ground state before analyzing its properties; however, its exponentially large Hilbert space has made such studies costly, if not prohibitive, on sufficiently large system sizes. Here, we propose an alternative strategy based upon the expectation values of an ensemble of operators and the elusive yet vital quantum constraints between them, where the search for ground-state properties simply equates to simple, classical constrained minimization. These quantum constraints are generally obtainable via machine learning on a large number of sample quantum many-body states systematically consistent with physical presumptions. We showcase our perspective on 1D fermion chains and spin chains for applicability, effectiveness, and several unique advantages, especially for strongly correlated systems, thermodynamic-limit systems, property designs, etc.Comment: 6 pages, 4 figure

Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties

A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach

RNAa-mediated overexpression of WT1 induces apoptosis in HepG2 cells

<p>Abstract</p> <p>Aim</p> <p>Recent studies have reported that double-stranded RNA (dsRNA) can activate gene expression by targeting promoter sequence in a process termed RNA activation. The present study was conducted to evaluate the potential of WT1 induction by small activating RNA targeting the WT1 promoter (dsWT1) in the treatment of hepatocellular carcinoma.</p> <p>Methods</p> <p>The human hepatocellular carcinoma cell line HepG2 was transfected with dsRNA by liposomes. The expression of mRNA and protein in cells were investigated using real-time reverse real-time quantitative PCR and Western blot, respectively. Cell viability and clonogenicity were determined by MTT assay and clonogenicity assay, respectively. Cell apoptosis was evaluated by flow-cytometric analysis.</p> <p>Results</p> <p>Expressions of WT1 mRNA and protein in dsWT1 treated HepG2 cells were significantly elevated. Inhibition of cell viability by dsWT1 was dose-dependent and time-dependent. Reduction of the number and size of colonies formed were found in dsWT1 treated cells. dsWT1 induced significant apoptosis in HepG2 cells. The decreased anti-apoptotic protein Bcl-2 and elevated pro-apoptotic protein Bak expression were detected in dsWT1 treated cells. The level of pro-caspase-3 remarkably decreased and cleaved caspase-3 and PARP fragment were also detected in dsWT1 treated cells.</p> <p>Conclusion</p> <p>These data show that RNAa-mediated overexpression of WT1 may have therapeutic potential in the treatment of hepatocellular carcinoma.</p