Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC

Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms

Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN.Comment: 9 pages, 9 figures, 5 table

Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks is trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model's performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.Comment: Accepted in IJC

Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects

Here we report a facile method to generate a high density of point defects in graphene on metal foil and show how the point defects affect the electronic structures of graphene layers. Our scanning tunneling microscopy (STM) measurements, complemented by first principle calculations, reveal that the point defects result in both the intervalley and intravalley scattering of graphene. The Fermi velocity is reduced in the vicinity area of the defect due to the enhanced scattering. Additionally, our analysis further points out that periodic point defects can tailor the electronic properties of graphene by introducing a significant bandgap, which opens an avenue towards all-graphene electronics.Comment: 4 figure

Strain Induced One-Dimensional Landau-Level Quantization in Corrugated Graphene

Theoretical research has predicted that ripples of graphene generates effective gauge field on its low energy electronic structure and could lead to zero-energy flat bands, which are the analog of Landau levels in real magnetic fields. Here we demonstrate, using a combination of scanning tunneling microscopy and tight-binding approximation, that the zero-energy Landau levels with vanishing Fermi velocities will form when the effective pseudomagnetic flux per ripple is larger than the flux quantum. Our analysis indicates that the effective gauge field of the ripples results in zero-energy flat bands in one direction but not in another. The Fermi velocities in the perpendicular direction of the ripples are not renormalized at all. The condition to generate the ripples is also discussed according to classical thin-film elasticity theory.Comment: 4 figures, Phys. Rev.

A Large-Scale Particle Image Velocimetry System Based on Dual-camera Field of View Stitching

Large-Scale Particle Image Velocimetry (LSPIV) is an effective method for the measurement of the river surface flow velocity. For the wide cross-section river, in the near-field area of river surface image there are small and clear targets. But the flow tracers are almost invisible in the far-field area because of the resolution limit of one single video camera, which makes it difficult to complete the velocimetry task. So a dual-camera based LSPIV system has been developed for monitoring the wide cross-section river. This system is based on two digital Internet Protocol (IP) video cameras either of which captures more than half of the river surface with high resolution. And then the developed system stitches the two images into one covering the entire wide cross-section rivers. In the far field of river, the accuracy of the vector increases from 20.4 % to 80.4 %

Angle-dependent van Hove singularities and their breakdown in twisted graphene bilayers

The creation of van der Waals heterostructures based on a graphene monolayer and other two-dimensional crystals has attracted great interest because atomic registry of the two-dimensional crystals can modify the electronic spectra and properties of graphene. Twisted graphene bilayer can be viewed as a special van der Waals structure composed of two mutual misoriented graphene layers, where the sublayer graphene not only plays the role of a substrate, but also acts as an equivalent role as the top graphene layer in the structure. Here we report the electronic spectra of slightly twisted graphene bilayers studied by scanning tunneling microscopy and spectroscopy. Our experiment demonstrates that twist-induced van Hove singularities are ubiquitously present for rotation angles theta less than about 3.5o, corresponding to moir\'e-pattern periods D longer than 4 nm. However, they totally vanish for theta > 5.5o (D < 2.5 nm). Such a behavior indicates that the continuum models, which capture moir\'e-pattern periodicity more accurately at small rotation angles, are no longer applicable at large rotation angles.Comment: 5 figure

Presence of Porphyromonas gingivalis in esophagus and its association with the clinicopathological characteristics and survival in patients with esophageal cancer

BACKGROUND: Mounting evidence suggests a causal relationship between specific bacterial infections and the development of certain malignancies. However, the possible role of the keystone periodontal pathogen, Porphyromonas gingivalis, in esophageal squamous cell carcinoma (ESCC) remains unknown. Therefore, we examined the presence of P. gingivalis in esophageal mucosa, and the relationship between P. gingivalis infection and the diagnosis and prognosis of ESCC. METHODS: The presence of P. gingivalis in the esophageal tissues from ESCC patients and normal controls was examined by immunohistochemistry using antibodies targeting whole bacteria and its unique secreted protease, the gingipain Kgp. qRT-PCR was used as a confirmatory approach to detect P. gingivalis 16S rDNA. Clinicopathologic characteristics were collected to analyze the relationship between P. gingivalis infection and development of ESCC. RESULTS: P. gingivalis was detected immunohistochemically in 61 % of cancerous tissues, 12 % of adjacent tissues and was undetected in normal esophageal mucosa. A similar distribution of lysine-specific gingipain, a catalytic endoprotease uniquely secreted by P. gingivalis, and P. gingivalis 16S rDNA was also observed. Moreover, statistic correlations showed P. gingivalis infection was positively associated with multiple clinicopathologic characteristics, including differentiation status, metastasis, and overall survival rate. CONCLUSION: These findings demonstrate for the first time that P. gingivalis infects the epithelium of the esophagus of ESCC patients, establish an association between infection with P. gingivalis and the progression of ESCC, and suggest P. gingivalis infection could be a biomarker for this disease. More importantly, these data, if confirmed, indicate that eradication of a common oral pathogen could potentially contribute to a reduction in the overall ESCC burden. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13027-016-0049-x) contains supplementary material, which is available to authorized users